Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness

Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness Davide Caldo, Silvia Bologna, Giorgio De Nunzio, Luana Conte, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1912577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background - Dynamic interplay between the patient collective consciousness and the subliminal affective content of digital information may play a critical role in emergence of chronic pain, within the combined perspective of somatic marker and complex adaptive system theoretical frames Goal - Testing Machine Learning (ML) algorithms accuracy to predictively discriminate back pain vs hip/knee osteoarthritis affective fingerprints of web pages Methods - Top 2000 internet pages related to the topics of interest were selected by relevance/popularity and submitted to automated sentiment analysis; Machine Learning algorithms classified the output Results - ML showed high discrimination accuracy predicting the page topic. The emotion Disgust emerged as the key discriminating factor Discussion - The new paradigm labeled “digital affective collective consciousness” (DACC) and the role of disgust in musculoskeletal disease are discussed; DACC may play a regulatory uninvestigated role of the emergence of health/illness conditions affecting subjects and communities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic pain is a very significant and costly problem throughout the industrialized world (Gatchel, 2014). Information acts as a critical blocking or favoring agent towards its emergence (Brown 2013 ). This notion is consistent with the Biopsychosocial (BPS) Model that suggests that a person's state of illness or health is not coincident with its organic substrate alteration, rather intertwined with psychological and social factors (Engel 1977 ). Emotion's critical role, outlined by decades of affective neuroscience findings, tightly integrated affective domains in the model (Sander, 2013 ). Chronic pain in neuroscientific literature has been observed with cerebral fMRI to be linked to activation of a number of nociceptive, cognitive and affective central nervous system circuitry, rather than just being the mere result of nociception, the damage signaling system from the peripheral body (Cauda 2014). The BPS model has been further evolved through integrations of notions from Ashby's law of requisite variety, Rothman's notion of multiple sufficient causes of a condition, networked interdependencies between system layers, psychological- neuroimmunological interplay and top-down causation in complex adaptive systems (philosophy of complex adaptive systems theory) (Sturmberg, 2021 ). The case for chronic pain within the complex adaptive system theory has been investigated in literature (Brown, 2013 ). An entirely new discipline called Social Neuroscience is based on neurophysiological (i.e. mirror neuron and internal simulation activity) and social evidence of the notion of collective consciousness (CC), the set of shared beliefs, ideas, and moral attitudes operating as a unifying force within society (Coombs, 2008), including collective emotions, emergent macrolevel affective processes that cannot be readily captured at the individual level (Goldenberg, 2020 ). The major source of information in the modern world is the internet (Hilbert, 2011 ). The interplay of CC with digital information is dynamic: users initially determine “success” of web information (pages) by visiting the ones more fitting their psychological drive; search engine algorithms (like Google’s) pagerank “promote” visibility of those pages by putting them at the top of the search engine results, closing a self reverberant circle for the emergence of a pool of socially validated digital information (Brin, 1998 ); since emotions are also determinants of behavior according to the somatic marker theory (Damasio 1991), the affective content of web pages should be carefully addressed by research. We propose to functionally label “digital affective collective consciousness” (DACC) the system including affective content of digital information in its dynamic interplay with users, under the general hypothesis that DACC has a potential role as a regulator of the emergence of chronic pain. In order for DACC to play such a role, specific emotions should be consistently expressed across web pages concerning the same topic; also in the case of a specific medical condition, they probably fit user strategy within the pursuit of homeostasis balance (Sachs 2015). Sentiment analysis (also known as opinion mining) is the systematic identification, extraction, quantification, and study of affective states using natural language processing, text analysis, computational linguistics and biometrics via computer science analysis (Hamborg, 2021 ). In this research 2000 top rating English language websites are analysed comparing two pooled conditions: first one is the “nonspecific” degenerative chronic lumbar back pain (LBP), characterized by high rates of failed surgical treatment; the other arthritis dependent chronic greater joint (hip and knee) pain, generally leading to arthroplasty. The hypothesis tested is that the supervised machine learning (ML) algorithms would accurately discriminate the topic of the original text by classifying the affective emotional fingerprint of internet pages produced by the automated sentiment analysis. Bringing the focus to this perspective establishes a paradigm for further research on bps pathogenesis of affections in the modern world, potential optimization of cures, development of new treatment and it could accelerate the steps from “episodic medicine” to continuous healthcare exploiting the potential of digital social data. Results Word cloud chart In this section are shown word cloud charts for the English language with the first 50 most frequent words respectively for back pain and hip prosthesis. Affective pathology pattern predictivity All the sub-datasets of internet documents were examined and compared, modeled as vectors of emotional scores (for joy, admiration, surprise, fear, disgust, anger, sadness, and interest) and labeled by health conditions of interest (“classes”), to assess if they significantly differed both at group level and at individual level. The statistical relevance of variables has been established. Nonetheless, relevance is not a synonym for discriminant power as used in classification and prediction (Bzdok, 2021): significant variables in a statistical model do not guarantee prediction performance, and non-significant attributes might reveal predictive. For this reason, both approaches have been followed: group-level association and the ML predictive assessment. Then health conditions were compared in a pairwise fashion, both graphically with per-class histograms and scatter plots, and computationally with significance tests (Mann-Whitney U-test) and a ML approach, Support Vector Machines (SVM) (Suthaharan, 2016). Some variables (in particular, disgust) showed a large discriminating power, which allow efficient training of a classifier (Table 1). Some pairs of variables, too, were discriminating when considered together (e.g. admiration and surprise). A few features were quite strongly correlated, such as disgust and sadness. Figure 3 shows the example case of English language, back pain vs hip prosthesis documents. In this example surprise and disgust are the two paired emotions and their peculiar distributions are signal of an important discriminating power Nonparametric statistical test and machine learning test (Support Vector Machine) Statistical non parametric tests (in particular Mann-Whitney U-test) applied to assess uni-variate emotional score group differences between documents for different health conditions, mostly fail in finding low p-values (low p-values allow us to reject the null hypothesis that the documents from two classes, e.g. back pain and hip prosthesis, come from the same distribution). ACCURACY Support Vector Machine classifier p_VALUE Mann-Whitney U-test back Pain vs hip Prosthesis 0.95195 0.4807 back Pain vs knee Prosthesis 0.95745 0.049122 DiscHerniation vs kneeProsthesis 0.93529 0.96352 DiscHerniation vs hipProsthesis 0.93605 0.06233 LBP vs hipProsthesis 0.93976 1.1942 LBP vs kneeProsthesis 0.94207 0.27725 Table 1. The first result column reports the classification accuracies of a linear SVM classifier trained and validated with a 5-fold cross validation scheme; the second one shows the p-values for the same combinations of health conditions, issued by the Mann-Whitney U-test. Here small p-values (0.90). Decision trees The case is the same shown in Figure 3, i.e. English language, back pain vs hip prosthesis. The graphics show the decision tree (Figure 4) and, in particular, the decision tree after pruning. Pruning is a tree compression technique that removes sections of the decision tree that are redundant and have little influence on the classification accuracy. Pruning reduces the classifier complexity and has two consequences: it reduces overfitting (possibly enhancing generalization) and improves explainability. Figure 5 shows an estimate of predictor importance, which is consistent with the trees. Discussion The somatic marker hypothesis proposes that emotional processes guide behavior , particularly decision-making, with the affective apparatus supporting rational thought (Damasio 1991). Individuals enforce behaviors tending to homeostatic balance (the state of steady internal, physical and chemical conditions maintained by living systems ), driven by the general positive valence feeling identified as “pleasure”; the opposite “suffering” is the feeling related to diversion from homeostatic imbalance conditions (Damasio, 2000). Noteworthy, pursuit of homeostatic balance (and pleasure) can implement articulate strategies, incorporating negative valence emotions (Sachs, 2015). Highly sophisticated socio-cultural issues involve emotions, such as the emergence of moral judgment (Keonig, 2007). It has been proposed that multiple people can converge on the same emotion pattern when exposed to digital media (Goldenberg, 2020). The BPS model was proposed for managing degenerative musculoskeletal chronic pain , with emotions recognized as integral to emergence, conceptualization, assessment and treatment of persistent pain (Lumley, 2011). The main BPS model limit remains the lack of tools for clinical usability: thus the heuristic value of a reductionistic “mechanical” approach largely prevails in clinical settings (Doley, 2017). However chronic pain treatment based on these premises showed serious limits, such as failure of both analgesic medications interfering with nociception and surgical strategy based on removal of degenerated organic substrate; the former led to the phenomenon of “opioid epidemics” (severe complication by diffusion/overuse of opioid) (Jones, 2018), while failed back surgery rate for back pain is between 20 and 40% (Thomason, 2013). All in all, 20.4% of adults in the US are affected by chronic pain, 8% in a severe form according to CDC (Dahlamer, 2016). To give back depth and truth to pain experience it is necessary to reinterpret it within the updated BPS approach. By the end of the eighties of the XX century less than 1% of the world’s information was archived in a digital format, whereas in 2007 it reached 94% (Hilbert, 2011); in January 2022, 4.66 billion people accessed the internet; GWI’s survey also finds that 25.9 percent of working-age internet users check health symptoms online every week (DataReportal, 2022). Digitalization shifted from a nearly irrelevant share to all health knowledge corpus, coupled with widespread access to the internet, dramatically changing the scenario of healthcare information. The digital information feeds the cognitive and affective knowledge of patient collectivity. It has been suggested that the bps model must evolve to include a “digital” component, developing a ‘biopsychosocial-digital’ approach to health (Ahmadvand, 2018). Furthermore, Machine Learning algorithms showed predictive accuracy in many biology, medicine, and even social complex system applications; precision medicine in the 21st century strives for accurate prediction of what is beneficial for individual patients; prediction, as opposed to association, comes into play when forecasting outcomes that are yet unobserved; predictivity rather than statistical association can lead the way to precision medicine (Bzdok, 2021). Also relying on predictivity represents the best approach to complement explanatory models and setting new neuroscientific theoretical grounding (Dolce, 2020). Machine learning algorithms have been previously applied to the field of pain affection (Goldstein, 2020). The DACC defines the affective subset of the “virtual collective consciousness'', in dynamic interplay with CC. The original concept derived from humanities and was initially restricted to social networking influencing behavior (Cheok 2015, Boire 2000). Sentiment analysis is a useful method to characterize the DACC; such assessment is extremely relevant in pathologies with a major psychosocial content. Within the biopsychosocial frame for the degenerative disorders, different musculoskeletal pathologies have different presentations and a specific balance of biopsychosocial components. For instance, fibromyalgia is the musculoskeletal disease mostly driven by central nervous predispositions ( Phillips and Clauw, 2011 ); on the other end of the spectrum, hip and knee osteoarthritis have a larger peripheral nociceptor contribution, as suggested by a better success rate of pain relief with joint replacement surgery ( Buchbinder et al., 2014 ). LBP generally don't relate to a specific major organic lesion, rather the result of a large combination of minor degenerative lesions with a variety of organic substrate presentations: this “nonspecificity” parallels fibromyalgia. LBP is the main source of chronic pain and disability in the world (Blyth et al 2019), linked in a controversial way with emotional regulation, somatosensory amplification and rumination in negative affects or dysfunctional beliefs (Le Borgne, 2017). LBP affective states more frequently reported are fear (Wertly, 2014), anger and sadness in a frame of catastrophism, anxiety, or depression (Alyousef, 2018, Burns, 2006). Fear has a direct effect on the outcome, influencing behavior: fear of pain and/or injury/movement leads to movement avoidance, and it is possibly implicated in the transition from acute to chronic and persistence of disabling LBP (Trinderup, 2018). Anger is another leading emotion in many LBP studies, with greater effects on chronic pain severity than sadness: it is shown that people who tend to express anger and who exhibit high pain sensitivity could be characterized by deficits in endogenous inhibitory mechanisms (Bruehl et al., 2002). A symptom-specific reactivity model showed that anger arousal may lead to increases in muscle tension near the site of injury, and thereby increase pain; increases in lower paraspinal muscle tension are higher in anger than in sadness, and patients with elevated anger expressiveness showed greater increases in muscle tension (Burns, 2006). Estimated 2010 prevalence of total hip and total knee replacement among the total U.S. population was 0.83% and 1.52%, respectively. (Maradit Kremers, 2015). Chronic pain despite joint replacement is not uncommon, affecting approximately 10% of patients after total hip replacement and 20% of patients after total knee replacement (Wylde, 2015). The related emotion reported in literature is fear (Unver, 2014), altogether with withdrawal and depression (Moore, 2022). Psychological and structural factors interact exacerbating pain perception (Pan, 2018, Nwanko 2021). From our digital information analysis, major relevance for disgust emerged as a key discriminating factor between LBP and hip/knee affection related internet pages; disgusts acts as the first alternative in all the ML decision trees generated (see Methods and Results); in some cases the degree of disgust alone identifies the original topic from the affective pattern; in other cases ML rely on a combination of disgust (prevalent determinant) with surprise, joy or sadness. The difference in disgust intensity in the two affective patterns of the two pathologies selected may reflect the different biopsychosocial profile of the two. Disgust in digital text can be described as one of the mathematical “bifurcation” elements integral to complex adaptive nonlinear systems (Sturmberg 2021). Disgust is one of the basic emotions characterized by a strong sense of aversion and reluctance. It is associated with physical reactions (nausea, sweating and lowering blood pressure), as it is also considered originally an emotion with the function of disease avoidance (Oaeten, 2015). Disgust is an emotion of well-rooted evolutionary origin: animals have evolved a series of behaviors to reduce the risk of infection by pathogens, such as viruses, bacteria, multicellular parasites and their carriers (Loehle, 1995). Disgust may be argued to be an inherent reaction to invasive treatments, even unconsciously, by the author of the web pages. This observation doesn’t explain the “success” of pages assessed by relevance/popularity; indeed, disgust is a diverging emotion, it wouldn’t be expected to be associated at all to popularity, except as a negative valence emotion incorporated in a counterintuitive way in a more complex scheme (Sachs 2015). One issue drawing a parallel between the two phenomena of sad music and musculoskeletal informational sites is the potentially relevant role of the aesthetic component. It has been detailed by Sachs (2015) the component of attraction power based on the pure aesthetic power of the art; in that sense diverting emotions such as sadness or disgust could be felt as pleasurable in order of their aesthetic value alone. The authors are not aware of any published study concerning evaluation of the intrinsic aesthetic attraction power of medical websites. One of the goals assumed for people affected by chronic pain is the neurophysiological reaction to contain it. Emotion of disgust showed a tendency to lead to a delayed up complex regulation of immune-related functions, effects similar to the acute phase response to an infection; in Oateng study, immediately after a disgust induction, pain was reduced, but later it was increased leading to a final higher pain sensitivity (Oaten, 2015). It can be conjectured that the temporary decrease in pain sensitivity plays a role in internet pages' success in collecting “clicks”. If that is the case, the later sensitization to chronic pain may rather globally contribute to a negative outlook, influencing outcome; the information in this case could rather be globally acting as a noxious agent. Such a scenario calls for specific research, since it would raise serious concerns about the nature and global role of digital information, and its subliminar emotional effect; scientific, but also ethical and legal implications as much as public healthcare issues are raised, a notion far more alarming on a social layer of discussion that follows. A characteristic of people with chronic pain is avoidance: the “cognitive-behavioral fear-avoidance model” includes cognitive (idiosyncratic maladaptive beliefs on pain), affective (fear) and behavioral (avoidance) components (Vlaeyen et al., 2000); it can be argued that digital information of disgust could be involved in the “adaptive” characteristic of the pain system, in the “diverting from disease” impulse endorsing avoidance strategy. In real life plane movement is the target of avoidance, thus fear is the key emotion; in a virtual environment that could translate to an emotional strategy to avoid circumstances where motion is required by peer pressure to interact; without immediate need for motion avoidance disgust seems the most fitting emotion compatible with a patient's drive. Although disgust was first thought to be a motivation for humans to avoid only physical contaminants, it has since been applied to moral and social moral contaminants as well. Likewise, when a group experiences someone who cheats, rapes, or murders another member of the group, its reaction is to shun or expel that person from the group, basically the same behaviour explicited when diverting from contaminating biologic fluids (Jones, 2008). When one experiences disgust, this emotion might signal that certain behaviors, objects, or people are to be avoided in order to preserve their purity . Socio-moral disgust occurs when ethical boundaries appear to be violated. This aspect focuses on human violations of the autonomy and dignity of others (e.g., discrimination). This kind of disgust is different from the core emotion: there was a divergence found in responses between the core elicitors of disgust and the socio-moral elicitors, suggesting that the makeup of core and socio-moral disgust may be different emotional constructs (Simpson, 2006). Horberg et al. found that disgust plays a role in the development and intensification of moral judgments of purity in particular. In other words, the feeling of disgust is often associated with a feeling that some image of what is pure has been violated (Horberg, 2009). Furthermore, disgust appears to be uniquely associated with purity judgments, not with what is just/unjust or what is harmful/caregiving, while other emotions such as fear, anger, and sadness are "unrelated to moral judgments of purity". The emotion of disgust can be hypothesized to serve as an effective mechanism following occurrences of negative social value, provoking repulsion, and desire for social distance. The origin of disgust can be defined by motivating the avoidance of offensive things, and in the context of a social environment , it can become an instrument of social avoidance. Disgust is known to reduce motivations for social interaction (Sherman, 2011). Again, the issue would call for specific research, as serious implications may be implied, social avoidance being itself part of the chronic pain system. Social interactions are a key component of wellbeing in the aging population, with the growth of emotional empathy serving as a compensation factor to cognitive age-related decadence of cognitive empathy (Beadle 2019). Some limits of the present work are acknowledged. Arbitrary pathologies were chosen for comparison, although their biopsychosocial distance is rooted in neuroscientific background. More tests need to be performed with different pathologic conditions, to fully comprehend the extent and generalize the relevance of the field, laying ground for more detailed theoretical models and subsequent potential computer science applications. The assessment was limited to sites, while the relative weight of social networks remains unknown. Quantitative analysis was performed for the English language only, as it is considered the most relevant language for digital information, and to contain language dependent and culture dependent biases; the quantitative analysis can be extended to other language frames. Only the presence of words attributed to specific emotions was taken into account, more advanced methods also consider text complexities such as negation or sarcasm, or emotional word valence that can change according to context and domain. Actually, several works adopt this simplified methodology which in practice can work adequately (Samothrakis, 2015). In conclusion, specific emotions shown are consistently expressed across web pages concerning the same medical condition; disgust was shown to be the emotion with more relevant discriminative power between the two examined conditions, showing the different biopsychosocial profile may reflect a noticeable difference in respective affective profile. Artificial Intelligence demonstrated predictive accuracy on the specific affective digital fingerprint. The notion of DACC was described as a conceptual framework for research in the field. Future perspectives may include applying the concept to other digital arenas (such as social network), other bps pathologies, building models behavioral analysis, and exploiting results potential to finally overcome limitations of treatments based on mechanical reductionism; the process may lead to better healthcare, application of modern neuroscience evidence to medicine, optimization of cures concerning major medicine issues, a revision of some fundamental definitions such as reductionist conceptualization of chronic pain, and a greater prosperity of community; semi-real time sentiment analysis monitoring of digital information from patients (possibly through social network) hold the potential to be part of the roadmap from ”episodic medicine” (reactive medicine) to modern continuous healthcare. In order to do so, predictive capability towards valuable endpoints is the key, likely related to development of artificial intelligence and pervasive monitoring technologies. Methods Study design and goals The focus consisted of a list of keywords regarding topics related to the orthopedics affection of interest. In particular: back pain, hip replacement, hip arthritis, knee arthroplasty, arthritis in knee, low back pain. The list of keywords was independently identified by the 3 authors that are specialized in spine (DC), knee surgery (EK), and hip surgery (RF). Content and data from about 2000 URLs [G2] were converted to raw text, exported as a CSV file. The gathered data contains five language texts related to four categories [G3] of orthopedics diseases or health conditions (back pain, hip prosthesis, knee prosthesis and low-back pain). The available technologies and resources used for this study were: ● SEMrush Competitors Research (SEM) to identify relevant internet sources ● Data from the GDELT Project for the construction of the baseline (background) corpus with which to normalize the data obtained ● SenticNet for sentiment analysis ● R package version 4.03 and Python 3.0 for data processing and analysis The following paragraphs describe in detail the steps, the data in the document, the tools used and the calculations performed. Identifying relevant sources Relevant internet sources were analysed using a tool called “SEMrush Competitors Research” (SEM); SEMrush is a software designed for companies to run digital marketing, Search Engine Optimization (SEO), Search Engine Marketing (SEM), pay-per-click, social media, and content marketing campaigns. SEMrush can identify trends that occur within a web niche and rank performance on a content-specific base. Following parameters were evaluated by SEM software to each internet source: Page AS: SEMrush standard metric used to measure the overall quality of the URL and influence on SEO. The score is based on the number of backlinks, referring domains, organic search traffic, and other cases. It’s a tool to measure the impact of a webpage or domain’s links. Authority Score is a compound domain score that grades the overall quality of a website. The higher the score, the more assumed weight a domain’s or webpage’s backlinks could have. Ref Domain: are a website that links out to another website whose backlink profile you analyze. When Google measures the trust of a domain from its backlinks, the search engine actually weighs having a high number of referring domains. Note: the total number of referring domains that have at least one link pointing to a given URL. SEMrush only consider the domains it has seen in the last few months. Backlinks: are links from one website to another. Search engines like Google use backlink as a ranking signal. Note: total number of backlinks pointing to a given URL. SEMrush only takes into account the backlinks it has seen in the last few months. For clarity, for a given web resource, a Backlink is a link from some other website (the referrer) to that web resource (the referent). A web resource may be (for example) a website , web page , or web directory . A backlink is a reference comparable to a citation . Search Traffic: The term “search traffic” refers to the entire traffic from various visitor sources through a specific medium. Note: the amount of estimated organic traffic brought to a given URL with the keyword analysed for a given time interval. URL Keyword: The number of keywords for which a given URL ranks in search results. Semantic Analysis - Senticnet Sentiment analysis (opinion mining, emotion AI) was here used to analyze sentences in healthcare resources to get words related to emotions. In the present study was used SenticNet ( https://sentic.net ) a multi-disciplinary approach to opinion mining at the crossroads between affective and common sense computing that combines semiotics, psychology, linguistics, and machine learning elements. Sentic computing, as opposed to statistical sentiment analysis, is a multi-disciplinary paradigm that focuses on a semantic-preserving representation of natural language concepts and sentence structure. Rather than depending exclusively on word co-occurrence frequencies, it accomplishes polarity identification and emotion recognition by leveraging the denotative and connotative information associated with words and multiword expressions. SenticNet is based on the Hourglass of Emotions, an emotion categorisation model developed to properly express the affective information associated with natural language text (Cambria 2012). Using this categorization, feelings are reorganized around four independent dimensions with different levels of activation that make up the total emotional state of mind. Affective states are classified into four dimensions - Pleasantness, Attention, Sensitivity and Attitude. Each of the four affective dimensions is characterized by six levels of activation, called "sentic levels' “, which determine the intensity of the emotion. In the Aptitude dimension can be found l oathing, disgust, boredom, acceptance, trust, admiration . In Pleasantness , grief, sadness, pensiveness, serenity, joy, ecstasy . In Sensitivity dimension, terror, fear, apprehension, annoyance, anger, rage . Finally, in the Attention state there are amazement, surprise, distraction, interest, anticipation, vigilance . BabelSenticNet (Vilares, 2018) is a multilingual concept-level knowledge base for sentiment analysis based on SenticNet for emotion recognition and the output returned us joy, admiration, surprise, fear, disgust, anger, sadness, interest. It has been used for multilingual analysis. For sentiment analysis Natural language processing (NLP) was used. The system can then extract accurate information and insights from the papers and categorize and organize them (Feldman, 1999). The text has been prepared with 3 processes: Tokenization, is the process of breaking down a given text into the smallest element in a sentence, termed token. Output: ”Hip”, ”replacement”, ”surgery”,”can”, ”help” , ”relieve”. Lemmatization, the process of discovering the normal form of an original word in the dictionary. Part of Speech Tagging, Labeling words in a text according to their word kinds is known as Part of Speech Tagging (POS-Tag, that is noun, adjective, adverb, verb, Etc.). It is a method for transforming a sentence into a list of words or tuples. Then with Sentiment analysis a systematic identification, extraction, quantification, and study of affective states was achieved. With the emotion variables have also been counted other variables such as n sentences, n words, n content words, etc. and stored them in a spreadsheet as a local file. Documents were grouped by condition. Each partial dataset of documents from a particular group was modeled by a dense matrix obtained by stacking the emotional vectors. Columns pertained to emotions and the rows indexed documents.. Nonparametric statistical test and machine learning test (Support Vector Machine) Statistical comparisons were performed in the English language with statistical non parametric tests (Mann-Whitney U-test, repeated three times) and using Linear Machine Learning SVM (Support Vector Machine) according to Sutharan (2016) and Decisional Trees. Using Machine Learning to classify the documents allowed us to find many cases with classification accuracy (assessed with a 5-fold cross validation scheme) higher than 0.90, which tells us that the document emotional content has a peculiar pattern, in each class of a pair of health conditions, making it different and recognizable. Classification was performed also with a decision tree, with the purpose of obtaining an explainable result (Islam, 2022). Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request. Code availability statement The code used to generate results reported in the manuscript that are central to the main claims are available from the corresponding authors upon reasonable request. References Ahmadvand A, Gatchel R, Brownstein J, Nissen L (2018) The Biopsychosocial-Digital Approach to Health and Disease: Call for a Paradigm Expansion. 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D., Dieppe, P., & Blom, A. W. (2015). Preoperative widespread pain sensitization and chronic pain after hip and knee replacement: a cohort analysis. Pain, 156(1), 47–54. https://doi.org/10.1016/j.pain.0000000000000002 Young G., Chapman C. R. (2007). “Pain, affect, nonlinear dynamical systems, and chronic pain: bringing order to disorder,” in Causality of Psychological Injury eds Young G., Chapman C. R. (New York, NY: Springer;) 197–241 Declarations Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported. Material and correspondence should be addressed to corresponding authors. Additional Declarations There is NO Competing Interest. 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Some pairs of variables, too, are discriminating when considered together (e.g. admiration and surprise). A few features are quite strongly correlated, such as disgust and sadness.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1912577/v1/de389491264e082dc50a607e.png"},{"id":24681597,"identity":"97a3cb01-8906-461b-afcd-037895592df0","added_by":"auto","created_at":"2022-08-02 17:23:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316841,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree before and after pruning, for English language, back pain vs hip prosthesis.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1912577/v1/ce5d37538897585783900d68.png"},{"id":24681596,"identity":"35fb11e2-d5cb-421f-8823-91cfc889c864","added_by":"auto","created_at":"2022-08-02 17:23:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15859,"visible":true,"origin":"","legend":"\u003cp\u003eEstimate of predictor importance, for English language, back pain vs hip prosthesis.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1912577/v1/a80e912dd2056385e23274e5.png"},{"id":24961018,"identity":"d9978cca-e2ca-40d0-a645-abad08627f4c","added_by":"auto","created_at":"2022-08-09 07:31:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":808889,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1912577/v1/3a3d5a11-abdf-4108-9423-2cef624300eb.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic pain is a very significant and costly problem throughout the industrialized world (Gatchel, 2014). Information acts as a critical blocking or favoring agent towards its emergence (Brown \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This notion is consistent with the Biopsychosocial (BPS) Model that suggests that a person's state of illness or health is not coincident with its organic substrate alteration, rather intertwined with psychological and social factors (Engel \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Emotion's critical role, outlined by decades of affective neuroscience findings, tightly integrated affective domains in the model (Sander, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Chronic pain in neuroscientific literature has been observed with cerebral fMRI to be linked to activation of a number of nociceptive, cognitive and affective central nervous system circuitry, rather than just being the mere result of nociception, the damage signaling system from the peripheral body (Cauda 2014). The BPS model has been further evolved through integrations of notions from Ashby's law of requisite variety, Rothman's notion of multiple sufficient causes of a condition, networked interdependencies between system layers, psychological- neuroimmunological interplay and top-down causation in complex adaptive systems (philosophy of complex adaptive systems theory) (Sturmberg, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The case for chronic pain within the complex adaptive system theory has been investigated in literature (Brown, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn entirely new discipline called Social Neuroscience is based on neurophysiological (i.e. mirror neuron and internal simulation activity) and social evidence of the notion of collective consciousness (CC), the set of shared beliefs, ideas, and moral attitudes operating as a unifying force within society (Coombs, 2008), including collective emotions, emergent macrolevel affective processes that cannot be readily captured at the individual level (Goldenberg, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The major source of information in the modern world is the internet (Hilbert, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The interplay of CC with digital information is dynamic: users initially determine \u0026ldquo;success\u0026rdquo; of web information (pages) by visiting the ones more fitting their psychological drive; search engine algorithms (like Google\u0026rsquo;s) pagerank \u0026ldquo;promote\u0026rdquo; visibility of those pages by putting them at the top of the search engine results, closing a self reverberant circle for the emergence of a pool of socially validated digital information (Brin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); since emotions are also determinants of behavior according to the somatic marker theory (Damasio 1991), the affective content of web pages should be carefully addressed by research. We propose to functionally label \u0026ldquo;digital affective collective consciousness\u0026rdquo; (DACC) the system including affective content of digital information in its dynamic interplay with users, under the general hypothesis that DACC has a potential role as a regulator of the emergence of chronic pain. In order for DACC to play such a role, specific emotions should be consistently expressed across web pages concerning the same topic; also in the case of a specific medical condition, they probably fit user strategy within the pursuit of homeostasis balance (Sachs 2015).\u003c/p\u003e \u003cp\u003eSentiment analysis (also known as opinion mining) is the systematic identification, extraction, quantification, and study of affective states using natural language processing, text analysis, computational linguistics and biometrics via computer science analysis (Hamborg, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research 2000 top rating English language websites are analysed comparing two pooled conditions: first one is the \u0026ldquo;nonspecific\u0026rdquo; degenerative chronic lumbar back pain (LBP), characterized by high rates of failed surgical treatment; the other arthritis dependent chronic greater joint (hip and knee) pain, generally leading to arthroplasty. The hypothesis tested is that the supervised machine learning (ML) algorithms would accurately discriminate the topic of the original text by classifying the affective emotional fingerprint of internet pages produced by the automated sentiment analysis. Bringing the focus to this perspective establishes a paradigm for further research on bps pathogenesis of affections in the modern world, potential optimization of cures, development of new treatment and it could accelerate the steps from \u0026ldquo;episodic medicine\u0026rdquo; to continuous healthcare exploiting the potential of digital social data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eWord cloud chart\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn this section are shown word cloud charts for the English language with the first 50 most frequent words respectively for back pain and hip prosthesis.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAffective pathology pattern predictivity\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll the sub-datasets of internet documents were examined and compared, modeled as vectors of emotional scores (for joy, admiration, surprise, fear, disgust, anger, sadness, and interest) and labeled by health conditions of interest (\u0026ldquo;classes\u0026rdquo;), to assess if they significantly differed both at group level and at individual level. The statistical relevance of variables has been established. Nonetheless, \u0026nbsp; relevance is not a synonym for discriminant power as used in classification and prediction (Bzdok, 2021): significant variables in a statistical model do not guarantee prediction performance, and non-significant attributes might reveal predictive. For this reason, both approaches have been followed: group-level association and the ML predictive assessment.\u003c/p\u003e\n\u003cp\u003eThen health conditions were compared in a pairwise fashion, both graphically with per-class histograms and scatter plots, and computationally with significance tests (Mann-Whitney U-test) and a ML approach, Support Vector Machines (SVM) (Suthaharan, 2016).\u003c/p\u003e\n\u003cp\u003eSome variables (in particular, disgust) showed a large discriminating power, which allow efficient training of a classifier (Table 1). Some pairs of variables, too, were discriminating when considered together (e.g. admiration and surprise). A few features were quite strongly correlated, such as disgust and sadness.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the example case of English language, back pain vs hip prosthesis documents. In this example surprise and disgust are the two paired emotions and their peculiar distributions are signal of an important discriminating power\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNonparametric statistical test and machine learning test (Support Vector Machine)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eStatistical non parametric tests (in particular Mann-Whitney U-test) applied to assess uni-variate emotional score group differences between documents for different health conditions, mostly fail in finding low p-values (low p-values allow us to reject the null hypothesis that the documents from two classes, e.g. back pain and hip prosthesis, come from the same distribution).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable style=\"width: 3.7e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border: 1pt solid rgb(204, 204, 204);padding: 2pt;height: 39pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: rgb(204, 204, 204) rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: solid solid solid none;border-width: 1pt 1pt 1pt medium;border-image: none 100% / 1 / 0 stretch;background: rgb(217, 217, 217) none repeat scroll 0% 0%;padding: 2pt;height: 39pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eACCURACY\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eSupport Vector Machine classifier\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: rgb(204, 204, 204) rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: solid solid solid none;border-width: 1pt 1pt 1pt medium;border-image: none 100% / 1 / 0 stretch;background: rgb(217, 217, 217) none repeat scroll 0% 0%;padding: 2pt;height: 39pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003ep_VALUE Mann-Whitney U-test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eback Pain vs hip Prosthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.95195\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.4807\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eback Pain vs knee Prosthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.95745\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.049122\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eDiscHerniation vs kneeProsthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.93529\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.96352\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eDiscHerniation vs hipProsthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.93605\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.06233\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eLBP vs hipProsthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.93976\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e1.1942\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204);border-style: none solid solid;border-width: medium 1pt 1pt;border-image: none 100% / 1 / 0 stretch;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003eLBP vs kneeProsthesis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112.5pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.94207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96.75pt;border-color: currentcolor rgb(204, 204, 204) rgb(204, 204, 204) currentcolor;border-style: none solid solid none;border-width: medium 1pt 1pt medium;background: white none repeat scroll 0% 0%;padding: 2pt;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;line-height:115%;color:black;\"\u003e0.27725\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:justify;'\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;margin-bottom:10.0pt;text-align:justify;'\u003eTable 1. The first result column reports the classification accuracies of a linear SVM classifier trained and validated with a 5-fold cross validation scheme; the second one shows the p-values for the same combinations of health conditions, issued by the Mann-Whitney U-test. Here small p-values (\u0026lt;0.05) \u0026nbsp;(which reject the null hypothesis of the same distribution at 95% confidence level) are compared with large prediction accuracies (arbitrarily \u0026gt;0.90).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDecision trees\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe case is the same shown in Figure 3, i.e. English language, back pain vs hip prosthesis. The graphics show the decision tree (Figure 4) and, in particular, the decision tree after pruning. Pruning is a tree compression technique that removes sections of the decision tree that are redundant and have little influence on the classification accuracy. Pruning reduces the classifier complexity and has two consequences: it reduces overfitting (possibly enhancing generalization) and improves explainability. Figure 5 shows an estimate of predictor importance, which is consistent with the trees.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe somatic marker hypothesis proposes that\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Emotion\"\u003eemotional\u003c/a\u003e processes guide\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Behavior\"\u003ebehavior\u003c/a\u003e, particularly decision-making, with the affective apparatus supporting rational thought (Damasio 1991). Individuals enforce behaviors tending to homeostatic balance (the state of steady internal,\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Physics\"\u003ephysical\u003c/a\u003e and\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Chemistry\"\u003echemical\u003c/a\u003e conditions maintained by\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Organism\"\u003eliving systems\u003c/a\u003e), driven by the general positive valence feeling identified as \u0026ldquo;pleasure\u0026rdquo;; the opposite \u0026ldquo;suffering\u0026rdquo; is the feeling related to diversion from homeostatic imbalance conditions (Damasio, 2000). Noteworthy, pursuit of homeostatic balance (and pleasure) can implement articulate strategies, incorporating negative valence emotions (Sachs, 2015). Highly sophisticated socio-cultural issues involve emotions, such as the emergence of moral judgment (Keonig, 2007). It has been proposed that multiple people can converge on the same emotion pattern when exposed to digital media (Goldenberg, 2020).\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;BPS model was proposed for managing degenerative musculoskeletal\u0026nbsp;\u003ca href=\"https://www.physio-pedia.com/Chronic_pain_and_the_brain\"\u003echronic pain\u003c/a\u003e, with emotions recognized as integral to emergence, conceptualization, assessment and treatment of persistent pain (Lumley, 2011). The main BPS model limit remains the lack of tools for clinical usability: thus the heuristic value of a reductionistic \u0026ldquo;mechanical\u0026rdquo; approach largely prevails in clinical settings (Doley, 2017). However chronic pain treatment based on these premises showed serious limits, such as failure of both analgesic medications interfering with nociception and surgical strategy based on removal of degenerated organic substrate; the former led to the phenomenon of \u0026ldquo;opioid epidemics\u0026rdquo; (severe complication by diffusion/overuse of opioid) (Jones, 2018), while failed back surgery rate for back pain is between 20 and 40% (Thomason, 2013). All in all, 20.4% of adults in the US are affected by chronic pain, 8% in a severe form according to CDC (Dahlamer, 2016). To give back depth and truth to pain experience it is necessary to reinterpret it within the updated BPS approach.\u0026nbsp;By the end of the eighties of the XX century less than 1% of the world\u0026rsquo;s information was archived in a digital format, whereas in 2007 it reached 94% (Hilbert, 2011); in January 2022, 4.66 billion people accessed the internet; GWI\u0026rsquo;s survey also finds that 25.9 percent of working-age internet users check health symptoms online every week (DataReportal, 2022). \u0026nbsp;Digitalization shifted from a nearly irrelevant share to all health knowledge corpus, coupled with widespread access to the internet, dramatically changing the scenario of healthcare information. The digital information feeds the cognitive and affective knowledge of patient collectivity.\u0026nbsp;It has been suggested that the bps model must evolve to include a \u0026ldquo;digital\u0026rdquo; component, developing a \u0026lsquo;biopsychosocial-digital\u0026rsquo; approach to health (Ahmadvand, 2018). Furthermore, Machine Learning algorithms showed predictive accuracy in many biology, medicine, and even social complex system applications; precision medicine in the 21st century strives for accurate prediction of what is beneficial for individual patients; prediction, as opposed to association, comes into play when forecasting outcomes that are yet unobserved; predictivity rather than statistical association can lead the way to precision medicine (Bzdok, 2021). \u0026nbsp;Also relying on predictivity represents the best approach to complement explanatory models and setting new neuroscientific theoretical grounding (Dolce, 2020). \u0026nbsp;Machine learning algorithms have been previously applied to the field of pain affection (Goldstein, 2020). The DACC defines the affective subset of the \u0026ldquo;virtual collective consciousness\u0026apos;\u0026apos;, in dynamic interplay with CC. The original concept derived from humanities and was initially restricted to social networking influencing behavior (Cheok 2015, Boire 2000). Sentiment analysis is a useful method to characterize the DACC; such assessment is extremely relevant in pathologies with a major psychosocial content.\u0026nbsp;Within the biopsychosocial frame for the degenerative disorders, different musculoskeletal pathologies have different presentations and a specific balance of biopsychosocial components. For instance, fibromyalgia is the musculoskeletal disease mostly driven by central nervous predispositions (\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529956/#R137\"\u003ePhillips and Clauw, 2011\u003c/a\u003e); on the other end of the spectrum, hip and knee osteoarthritis have a larger peripheral nociceptor contribution, as suggested by a better success rate of pain relief with joint replacement surgery (\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529956/#R27\"\u003eBuchbinder et al., 2014\u003c/a\u003e). LBP generally don\u0026apos;t relate to a specific major organic lesion, rather the result of a large combination of minor degenerative lesions with a variety of organic substrate presentations: this \u0026ldquo;nonspecificity\u0026rdquo; parallels fibromyalgia.\u0026nbsp;LBP is the main source of chronic pain and disability in the world (Blyth et al 2019), linked in a controversial way with emotional regulation, somatosensory amplification and rumination in negative affects or dysfunctional beliefs (Le Borgne, 2017). LBP affective states more frequently reported are fear (Wertly, 2014), anger and sadness in a frame of catastrophism, anxiety, or depression (Alyousef, 2018, Burns, 2006). Fear has a direct effect on the outcome, influencing behavior: fear of pain and/or injury/movement leads to movement avoidance, and it is possibly implicated in the transition from acute to chronic and persistence of disabling LBP (Trinderup, 2018). Anger is another leading emotion in many LBP studies, with greater effects on chronic pain severity than sadness: it is shown that people who tend to express anger and who exhibit high pain sensitivity could be characterized by deficits in endogenous inhibitory mechanisms (Bruehl et al., 2002). A symptom-specific reactivity model showed that anger arousal may lead to increases in muscle tension near the site of injury, and thereby increase pain; increases in lower paraspinal muscle tension are higher in anger than in sadness, and patients with elevated anger expressiveness showed greater increases in muscle tension (Burns, 2006).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstimated 2010 prevalence of total hip and total knee replacement among the total U.S. population was 0.83% and 1.52%, respectively. (Maradit Kremers, 2015). Chronic pain despite joint replacement is not uncommon, affecting approximately 10% of patients after total hip replacement and 20% of patients after total knee replacement (Wylde, 2015). The related emotion reported in literature is fear (Unver, 2014), altogether with withdrawal and depression (Moore, 2022). Psychological and structural factors interact exacerbating pain perception (Pan, 2018, Nwanko 2021).\u003c/p\u003e\n\u003cp\u003eFrom our digital information analysis, major relevance for disgust emerged as a key discriminating factor between LBP and hip/knee affection related internet pages; disgusts acts as the first alternative in all the ML decision trees generated (see Methods and Results); in some cases the degree of disgust alone identifies the original topic from the affective pattern; in other cases ML rely on a combination of disgust (prevalent determinant) with surprise, joy or sadness. The difference in disgust intensity in the two affective patterns of the two pathologies selected may reflect the different biopsychosocial profile of the two. Disgust in digital text can be described as one of the mathematical \u0026ldquo;bifurcation\u0026rdquo; elements integral to complex adaptive nonlinear systems (Sturmberg 2021).\u003c/p\u003e\n\u003cp\u003eDisgust is one of the basic emotions characterized by a strong sense of aversion and reluctance. It is associated with physical reactions (nausea, sweating and lowering blood pressure), as it is also considered originally an emotion with the function of disease avoidance (Oaeten, 2015). Disgust is an emotion of well-rooted evolutionary origin: animals have evolved a series of behaviors to reduce the risk of infection by pathogens, such as viruses, bacteria, multicellular parasites and their carriers (Loehle, 1995).\u003c/p\u003e\n\u003cp\u003eDisgust may be argued to be an inherent reaction to invasive treatments, even unconsciously, by the author of the web pages. This observation doesn\u0026rsquo;t explain the \u0026ldquo;success\u0026rdquo; of pages assessed by relevance/popularity; indeed, disgust is a diverging emotion, it wouldn\u0026rsquo;t be expected to be associated at all to popularity, except as a negative valence emotion incorporated in a counterintuitive way in a more complex scheme (Sachs 2015).\u003c/p\u003e\n\u003cp\u003eOne issue drawing a parallel between the two phenomena of sad music and musculoskeletal informational sites\u0026nbsp;is the potentially relevant role of the aesthetic\u0026nbsp;component. It has been detailed by Sachs (2015) the component of attraction power based on the pure aesthetic power of the art; in that sense diverting emotions such as sadness or disgust could be felt as pleasurable in order of their aesthetic value alone. \u0026nbsp;The authors are not aware of any published study concerning evaluation of the intrinsic aesthetic attraction power of medical websites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of the goals assumed for people affected by chronic pain is the neurophysiological reaction to contain it. Emotion of disgust showed a tendency to lead to a delayed up complex regulation of immune-related functions, effects similar to the acute phase response to an infection; in Oateng study, immediately after a disgust induction, pain was reduced, but later it was increased leading to a final higher pain sensitivity (Oaten, 2015). It can be \u0026nbsp;conjectured that the temporary decrease in pain sensitivity plays a role in internet pages\u0026apos; success in collecting \u0026ldquo;clicks\u0026rdquo;. If that is the case, the later sensitization to chronic pain may rather globally contribute to a negative outlook, influencing outcome; the information in this case could rather be globally acting as a noxious agent. Such a scenario calls for specific research, since it would raise serious concerns about the nature and global role of digital information, and its subliminar emotional effect; scientific, but also ethical and legal implications as much as public healthcare issues are raised, a notion far more alarming on a social layer of discussion that follows.\u003c/p\u003e\n\u003cp\u003eA characteristic of people with chronic pain is avoidance: the \u0026ldquo;cognitive-behavioral fear-avoidance model\u0026rdquo; includes cognitive (idiosyncratic maladaptive beliefs on pain), affective (fear) and behavioral (avoidance) components (Vlaeyen et al., 2000); it can be argued that digital information of disgust could be involved in the \u0026ldquo;adaptive\u0026rdquo; characteristic of the pain system, in the \u0026ldquo;diverting from disease\u0026rdquo; impulse endorsing avoidance strategy.\u0026nbsp;In real life plane movement is the target of avoidance, thus fear is the key emotion; in a virtual environment that could translate to an emotional strategy to avoid circumstances where motion is required by peer pressure to interact; without immediate need for motion avoidance disgust seems the most fitting emotion compatible with a patient\u0026apos;s drive.\u003c/p\u003e\n\u003cp\u003eAlthough disgust was first thought to be a motivation for humans to avoid only physical contaminants, it has since been applied to moral and social moral contaminants as well. Likewise, when a group experiences someone who cheats, rapes, or murders another member of the group, its reaction is to shun or expel that person from the group, basically the same behaviour explicited when diverting from contaminating biologic fluids (Jones, 2008). When one experiences disgust, this emotion might signal that certain behaviors, objects, or people are to be avoided in order to preserve their\u0026nbsp;\u003ca href=\"https://en.wiktionary.org/wiki/pure\"\u003epurity\u003c/a\u003e. Socio-moral disgust occurs when \u0026nbsp;ethical boundaries appear to be violated. This aspect focuses on human violations of the autonomy and dignity of others (e.g., discrimination). This kind of disgust is different from the core emotion: there was a divergence found in responses between the core elicitors of disgust and the socio-moral elicitors, suggesting that the makeup of core and socio-moral disgust may be different emotional constructs (Simpson, 2006).\u0026nbsp;Horberg et al. found that disgust plays a role in the development and intensification of moral judgments of purity in particular. In other words, the feeling of disgust is often associated with a feeling that some image of what is pure has been violated (Horberg, 2009). Furthermore, disgust appears to be uniquely associated with purity judgments, not with what is just/unjust or what is harmful/caregiving, while other emotions such as fear, anger, and sadness are \u0026quot;unrelated to moral judgments of purity\u0026quot;. The emotion of disgust can be hypothesized to serve as an effective mechanism following occurrences of negative social value, provoking repulsion, and desire for social distance. The origin of disgust can be defined by motivating the avoidance of offensive things, and in the context of a\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Social_environment\"\u003esocial environment\u003c/a\u003e, it can become an instrument of social avoidance. Disgust is known to reduce motivations for\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Social_interaction\"\u003esocial interaction\u003c/a\u003e (Sherman, 2011). Again, the issue would call for specific research, as serious implications may be implied, social avoidance being itself part of the chronic pain system. Social interactions are a key component of wellbeing in the aging population, with the growth of emotional empathy serving as a\u0026nbsp;compensation factor to cognitive age-related decadence of cognitive empathy (Beadle 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome limits of the present work are acknowledged. Arbitrary pathologies were chosen for comparison, although their biopsychosocial distance is rooted in neuroscientific background. More tests need to be performed with different pathologic conditions, to fully comprehend the extent and generalize the relevance of the field, laying ground for more detailed theoretical models and subsequent potential computer science applications. The assessment was limited to sites, while the relative weight of social networks remains unknown. Quantitative analysis was performed \u0026nbsp;for the English language only, as it is considered the most relevant language for digital information, and to contain language dependent and culture dependent biases; the quantitative analysis can be extended to other language frames.\u0026nbsp;Only the presence of words attributed to specific emotions was taken into account, \u0026nbsp;more advanced methods also consider text complexities such as negation or sarcasm, or emotional word valence that can change according to context and domain. Actually, several works adopt this simplified methodology which in practice can work adequately (Samothrakis, 2015).\u003c/p\u003e\n\u003cp\u003eIn conclusion, specific emotions shown are consistently expressed across web pages concerning the same medical condition; disgust was shown to be the emotion with more relevant discriminative power between the two examined conditions, showing the different biopsychosocial profile may reflect a noticeable difference in respective affective profile. Artificial Intelligence demonstrated predictive accuracy on the specific affective digital fingerprint. The notion of DACC was described as a conceptual framework for research in the field. Future perspectives may include applying the concept to other digital arenas (such as social network), other bps pathologies, building models behavioral analysis, and exploiting results potential to finally overcome limitations of treatments based on mechanical reductionism; the process may lead to better healthcare, application of modern neuroscience evidence to medicine, optimization of cures concerning major medicine issues, a revision of some fundamental definitions such as reductionist conceptualization of chronic pain, and a greater prosperity of community; semi-real time sentiment analysis monitoring of digital information from patients (possibly through social network) hold the potential to be part of the roadmap from \u0026rdquo;episodic medicine\u0026rdquo; (reactive medicine) to modern continuous healthcare. In order to do so, predictive capability towards valuable endpoints is the key, likely related to development of artificial intelligence and pervasive monitoring technologies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eStudy design and goals\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe focus consisted of a list of keywords regarding topics related to the orthopedics affection of interest. In particular: \u0026nbsp;back pain, hip replacement, hip arthritis, knee arthroplasty, arthritis in knee, low back pain. The list of keywords was independently identified by the 3 authors that are specialized in spine (DC), knee surgery (EK), and hip surgery (RF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContent and data from about 2000 URLs\u0026nbsp;[G2]\u0026nbsp;were converted to raw text, exported as a CSV file. The gathered data contains five language texts related to four categories\u0026nbsp;[G3]\u0026nbsp;of orthopedics diseases or health conditions (back pain, hip prosthesis, knee prosthesis and low-back pain).\u003c/p\u003e\n\u003cp\u003eThe available technologies and resources used for this study were:\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;SEMrush Competitors Research (SEM) to identify relevant internet sources\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Data from the GDELT Project \u0026nbsp;for the construction of the baseline (background) corpus with which to normalize the data obtained\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;SenticNet for sentiment analysis\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;R package version 4.03 and Python 3.0 for data processing and analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following paragraphs describe in detail the steps, the data in the document, the tools used and the calculations performed.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIdentifying relevant sources\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelevant internet sources were analysed using a tool called \u0026ldquo;SEMrush Competitors Research\u0026rdquo; (SEM); SEMrush is a software designed for companies to run digital marketing, Search Engine Optimization (SEO), Search Engine Marketing (SEM), pay-per-click, social media, and content marketing campaigns. SEMrush can identify trends that occur within a web niche and rank performance on a content-specific base.\u003c/p\u003e\n\u003cp\u003eFollowing parameters were evaluated by SEM software to each internet source:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePage AS: SEMrush standard metric used to measure the overall quality of the URL and influence on SEO. The score is based on the number of backlinks, referring domains, organic search traffic, and other cases. It\u0026rsquo;s a tool to measure the impact of a webpage or domain\u0026rsquo;s links. \u0026nbsp;Authority Score is a compound domain score that grades the overall quality of a website. The higher the score, the more assumed weight a domain\u0026rsquo;s or webpage\u0026rsquo;s backlinks could have.\u003c/li\u003e\n \u003cli\u003eRef Domain: are a website that links out to another website whose backlink profile you analyze. When Google measures the trust of a domain from its backlinks, the search engine actually weighs having a high number of referring domains. Note: the total number of referring domains that have at least one link pointing to a given URL. SEMrush only consider the domains it has seen in the last few months.\u003c/li\u003e\n \u003cli\u003eBacklinks: are links from one website to another. Search engines like Google use backlink as a ranking signal. Note: total number of backlinks pointing to a given URL. SEMrush only takes into account the backlinks it has seen in the last few months. For clarity, for a given web resource, a Backlink is a\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Hyperlink\"\u003elink\u003c/a\u003e from some other\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Website\"\u003ewebsite\u003c/a\u003e (the referrer) to that web resource (the referent). A web resource may be (for example) a\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Website\"\u003ewebsite\u003c/a\u003e,\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Web_page\"\u003eweb page\u003c/a\u003e, or\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Web_directory\"\u003eweb directory\u003c/a\u003e.\u0026nbsp;A backlink is a \u003ca href=\"https://en.wikipedia.org/wiki/Reference\"\u003ereference\u003c/a\u003e comparable to a \u003ca href=\"https://en.wikipedia.org/wiki/Citation\"\u003ecitation\u003c/a\u003e.\u003c/li\u003e\n \u003cli\u003eSearch Traffic: The term \u0026ldquo;search traffic\u0026rdquo; refers to the entire traffic from various visitor sources through a specific medium. Note: the amount of estimated organic traffic brought to a given URL with the keyword analysed for a given time interval.\u003c/li\u003e\n \u003cli\u003eURL Keyword: The number of keywords for which a given URL ranks in search results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSemantic Analysis - Senticnet\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSentiment analysis (opinion mining, emotion AI) was here used to analyze sentences in healthcare resources to get words related to emotions. In the present study was used SenticNet (\u003ca href=\"https://sentic.net\"\u003ehttps://sentic.net\u003c/a\u003e) a multi-disciplinary approach to opinion mining at the crossroads between affective and common sense computing that combines semiotics, psychology, linguistics, and machine learning elements. Sentic computing, as opposed to statistical sentiment analysis, is a multi-disciplinary paradigm that focuses on a semantic-preserving representation of natural language concepts and sentence structure. Rather than depending exclusively on word co-occurrence frequencies, it accomplishes polarity identification and emotion recognition by leveraging the denotative and connotative information associated with words and multiword expressions.\u003c/p\u003e\n\u003cp\u003eSenticNet is based on the Hourglass of Emotions, an emotion categorisation model \u0026nbsp;developed to properly express the affective information associated with natural language text (Cambria 2012). Using this categorization, feelings are reorganized around four independent dimensions with different levels of activation that make up the total emotional state of mind. Affective states are \u0026nbsp;classified into four dimensions - Pleasantness, Attention, Sensitivity and Attitude. Each of the four affective dimensions is characterized by six levels of activation, called \u0026quot;sentic levels\u0026apos; \u0026ldquo;, which determine the intensity of the emotion. In the \u003cem\u003eAptitude\u003c/em\u003e dimension can be found l\u003cem\u003eoathing, disgust, boredom, acceptance, trust, admiration\u003c/em\u003e. In \u003cem\u003ePleasantness\u003c/em\u003e, \u003cem\u003egrief, sadness, pensiveness, serenity, joy, ecstasy\u003c/em\u003e. \u0026nbsp;In \u003cem\u003eSensitivity\u003c/em\u003e dimension, \u003cem\u003eterror, fear, \u0026nbsp;apprehension, annoyance, anger, rage\u003c/em\u003e. Finally, in the\u003cem\u003e\u0026nbsp;Attention\u0026nbsp;\u003c/em\u003estate there are \u003cem\u003eamazement, surprise, distraction, interest, anticipation, vigilance\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBabelSenticNet (Vilares, 2018) is a multilingual concept-level knowledge base for sentiment analysis based on SenticNet for emotion recognition and the output returned us \u0026nbsp;joy, admiration, surprise, fear, disgust, anger, sadness, interest. It has been used for multilingual analysis.\u003c/p\u003e\n\u003cp\u003eFor \u0026nbsp;sentiment analysis Natural language processing (NLP) was used.\u003c/p\u003e\n\u003cp\u003eThe system can then extract accurate information and insights from the papers and categorize and organize them (Feldman, 1999).\u0026nbsp;The text has been prepared with 3 processes: \u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTokenization, is the process of breaking down a given text into the smallest element in a sentence, termed token.\u0026nbsp;Output: \u0026rdquo;Hip\u0026rdquo;, \u0026rdquo;replacement\u0026rdquo;, \u0026rdquo;surgery\u0026rdquo;,\u0026rdquo;can\u0026rdquo;, \u0026rdquo;help\u0026rdquo; , \u0026rdquo;relieve\u0026rdquo;.\u003c/li\u003e\n \u003cli\u003eLemmatization, the process of discovering the normal form of an original word in the dictionary.\u003c/li\u003e\n \u003cli\u003ePart of Speech Tagging, Labeling words in a text according to their word kinds is known as Part of Speech Tagging (POS-Tag, that is noun, adjective, adverb, verb, Etc.). It is a method for transforming a sentence into a list of words or tuples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen with Sentiment analysis a systematic identification, extraction, quantification, and study of affective states was achieved. With the emotion variables have also been counted other variables such as n sentences, n words, n content words, etc. and stored them in a spreadsheet as a local file.\u003c/p\u003e\n\u003cp\u003eDocuments were grouped by condition. Each partial dataset of documents from a particular group was modeled by a dense matrix obtained by stacking the emotional vectors. Columns pertained to emotions and the rows indexed documents..\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNonparametric statistical test and machine learning test (Support Vector Machine)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eStatistical comparisons were performed in the English language with statistical non parametric tests (Mann-Whitney U-test, repeated three times) \u0026nbsp;and using Linear Machine Learning SVM (Support Vector Machine) according to Sutharan (2016) and Decisional Trees.\u003c/p\u003e\n\u003cp\u003eUsing Machine Learning to classify the documents allowed us to find many cases with classification accuracy (assessed with a 5-fold cross validation scheme) higher than 0.90, which tells us that the document emotional content has a peculiar pattern, in each class of a pair of health conditions, making it different and recognizable.\u003c/p\u003e\n\u003cp\u003eClassification was performed also with a decision tree, with the purpose of obtaining an explainable result (Islam, 2022).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCode availability statement\u003c/h2\u003e\n\u003cp\u003eThe \u0026nbsp;code used to generate results reported in the manuscript that are central to the main claims are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmadvand A, Gatchel R, Brownstein J, Nissen L (2018) The Biopsychosocial-Digital Approach to Health and Disease: Call for a Paradigm Expansion. 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(New York, NY: Springer;) 197\u0026ndash;241\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported.\u003c/p\u003e\n\u003cp\u003eMaterial and correspondence should be addressed to corresponding authors.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1912577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1912577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e- Dynamic interplay between the patient collective consciousness and the subliminal affective content of digital information may play a critical role in emergence of chronic pain, within the combined perspective of somatic marker and complex adaptive system theoretical frames\u003c/p\u003e\u003ch2\u003eGoal\u003c/h2\u003e \u003cp\u003e- Testing Machine Learning (ML) algorithms accuracy to predictively discriminate back pain vs hip/knee osteoarthritis affective fingerprints of web pages\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e- Top 2000 internet pages related to the topics of interest were selected by relevance/popularity and submitted to automated sentiment analysis; Machine Learning algorithms classified the output\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e- ML showed high discrimination accuracy predicting the page topic. The emotion Disgust emerged as the key discriminating factor\u003c/p\u003e\u003ch2\u003eDiscussion -\u003c/h2\u003e \u003cp\u003eThe new paradigm labeled \u0026ldquo;digital affective collective consciousness\u0026rdquo; (DACC) and the role of disgust in musculoskeletal disease are discussed; DACC may play a regulatory uninvestigated role of the emergence of health/illness conditions affecting subjects and communities\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-02 17:23:39","doi":"10.21203/rs.3.rs-1912577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7c731f7-6d2c-4af0-bde0-4570bbc3ec08","owner":[],"postedDate":"August 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-09T07:30:59+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-02 17:23:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1912577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1912577","identity":"rs-1912577","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}