Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model

Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model View ORCID Profile Luis Silva , Marcus Milani , Sohum Bindra , Salman Ikramuddin , Megan Tessmer , Kaylee Frederickson , Abhigyan Datta , View ORCID Profile Halil Ergen , Alex Stangebye , Dawson Cooper , Kompal Kumar , Jeremy Yeung , View ORCID Profile Kamakshi Lakshminarayan , View ORCID Profile Christopher D Streib doi: https://doi.org/10.1101/2025.04.30.25326777 Luis Silva 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America 2 Department of Neurology, University of Florida , Gainesville, Florida, United States of America MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis Silva For correspondence: luis.silva{at}neurology.ufl.edu Marcus Milani 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America M.S Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sohum Bindra 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Salman Ikramuddin 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America 3 Department of Neurology, University of Texas Health Sciences Houston, Houston, Texas, United States of America MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan Tessmer 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America BSN, RN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaylee Frederickson 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America CCC-SLP Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abhigyan Datta 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Halil Ergen 4 Department of Occupational Therapy, University of Minnesota , Minneapolis, Minnesota, United States of America PhD, PT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Halil Ergen Alex Stangebye 5 Acute Rehabilitation Unit, M Health Fairview , Minneapolis, Minnesota PT, DPT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dawson Cooper 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kompal Kumar 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeremy Yeung 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America MIDS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kamakshi Lakshminarayan 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America MBBS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kamakshi Lakshminarayan Christopher D Streib 1 Department of Neurology, University of Minnesota , Minneapolis, Minnesota, United States of America MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher D Streib Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Introduction The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studies. The mRS can be assessed retrospectively from electronic health records (EHR), though this process is labor-intensive and prone to inter-rater variability. Large language models (LLMs) have demonstrated potential in automating clinical text classification. We hypothesize that a fine-tuned LLM can analyze EHR text and classify mRS scores for clinical and research applications. Methods We performed a retrospective cohort study of patients admitted to a specialist stroke neurology service at a large academic hospital system between August 2020 and June 2023. Each patient’s medical record was reviewed at two time points: (1) hospital discharge and (2) approximately 90 days post-discharge. Two independent researchers assigned an mRS score at each time point. Two separate models were trained on EHR passages with corresponding mRS scores as labeled outcomes: (1) a multiclass model to classify all seven mRS scores and (2) a binary model to classify functional independence (mRS 0–2) versus non-independence (mRS 3–6). Four-fold cross-validation was conducted, using accuracy and Cohen’s kappa as model performance metrics. Results A total of 2,290 EHR passages with corresponding mRS scores were included in model training. The multiclass model—considering all seven scores of the mRS—attained an accuracy of 77% and a weighted Cohen’s Kappa of 0.92. Class-specific accuracy was highest for mRS 4 (90%) and lowest for mRS 2 (28%). The binary model—considering only functional independence vs non-independence —attained an accuracy of 92% and Cohen’s Kappa of 0.84. Conclusion Our findings demonstrate that LLMs can be successfully trained to determine mRS scores through EHR text analysis. With further advancements, fully automated LLMs could scale across large clinical datasets, enabling data-driven public health strategies and optimized resource allocation. Introduction The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studies 1 , 2 . The mRS is scored from 0 (no symptoms) to 6 (death), with higher scores indicating greater disability. It is determined based on a patient’s stroke deficits and ability to perform daily activities 3 . It has also been used in pivotal stroke trials as a binary outcome comparing functional independence (scores 0-2) versus non-independence (scores 3-6) 4 . In most instances, trained clinicians or researchers collect the mRS in real-time. Alternatively, it can be assessed retrospectively from electronic health records (EHR), though this process is labor-intensive and prone to inter-rater variability 5 – 8 . These limitations constrain stroke research by making it dependent on the availability of trained research staff and prevents use of existing clinical databases for research. Large language models (LLMs), such as GPT-4, are deep learning-based models that perform well in text classification and generation 9 , 10 . Use in medical research is expanding, with notable examples in neuroscience. GPT-4 has demonstrated 84% accuracy in localizing neurologic lesions and has also achieved a passing grade on the American Board of Psychiatry and Neurology examination 11 , 12 . However, GPT-4 performed poorly when assessing the Glasgow Coma Scale, the Intracranial Hemorrhage score, and the Hunt & Hess classifications 13 . These studies did not involve fine-tuning, a process in which the base model is trained on task-specific data to enhance performance. Classification of mRS scores from EHR text has been previously developed by Fernandez and colleagues 14 who, using a non-LLM model, achieved 59% accuracy, limiting its practical application in both clinical and research settings. We hypothesize that a fine-tuned LLM can analyze EHR text from inpatient and outpatient settings and classify mRS scores for clinical and research applications. We aimed to develop a tool capable of streamlining observational stroke research and reducing reliance on trained research staff. Methods We performed a retrospective cohort study of patients evaluated by a stroke neurology service at a large academic hospital system between August 2020 and June 2023, following approval from the University of Minnesota Institutional Review Board. Each patient’s medical record was reviewed at two time points: (1) hospital discharge and (2) approximately 90 days post-discharge, with follow-up notes selected within a window of 30 to 120 days. To minimize variability in follow-up timing, researchers were instructed to evaluate clinical notes recorded as close as possible to 90 days post-discharge. If no appropriate notes were available, they gradually expanded the search window in both directions, extending to a final range of 30 to 120 days post-discharge. At each time point, two independent researchers— trained and certified in mRS assessment—assigned an mRS score. mRS scoring followed the Rankin Focused Assessment, a structured checklist that standardizes patient evaluation through a question-answer format. Researchers assigned scores by answering predefined questions about functional status and stroke-related deficits 15 . Discrepancies in mRS scoring were resolved through discussion. If no consensus was reached, a third reviewer adjudicated the case. Additionally, researchers identified and collected one corresponding EHR passage from the clinical note deemed critical for determining the mRS score. In order to prevent data leakage, direct mentions of mRS scores could not be included. This process generated the study’s observational unit: a paired EHR passage and mRS score. Because multiple, distinct EHR passages can support a single mRS score, each patient could contribute up to four EHR passage– mRS score pairs to the dataset: two from the discharge summary and two from the follow-up visit (one from each researcher at each timepoint). If the two researchers initially disagreed on the mRS score before reaching consensus, only the correctly adjudicated mRS score-EHR passage was included in the data set. If a consensus could not be reached on the mRS score, both observational units were excluded from the analysis. Figure 1a illustrates EHR text collection, mRS scoring, and data inclusion decisions for model training. Download figure Open in new tab Figure 1. (a) Illustrative example of the data inclusion and exclusion process for each potential observational unit of a hypothetical patient. (b) Actual data inclusion and exclusion for LLM training within our study cohort To develop our model, we used an existing LLM as the basis—Gatortron. Created at the University of Florida, Gatortron was trained using real-world EHR notes from the UFHealth system 16 . This allows it to capture the nuances of EHR text, making it suitable for our research. We fine-tuned Gatortron on our dataset to classify patients’ mRS scores based on the paired EHR passage. Fine-tuning is the process of further training a pre-trained language model on a specific task—in this case, classifying mRS scores from EHR passages—to enable the model to adapt its general language understanding to the nuances of functional outcome assessment in stroke 17 . Two separate models were trained: (1) a multiclass model to classify all seven mRS scores and (2) a binary model to classify functional independence (mRS 0–2) versus non-independence (mRS 3–6). Four-fold cross-validation was conducted, using accuracy, unweighted, and weighted Cohen’s kappa as performance metrics. EHR passage collected by researchers was used in its original form, without additional preprocessing or normalization. During training, mRS classes were assigned training weights based on their overall frequency in the dataset—lower-frequency classes had proportionally higher weights. This approach balanced the dataset, reducing the likelihood of the model overfitting to more common cases. Additionally, each fold was stratified to prevent model overfitting. Appendix 1 contains detailed model information. The data supporting this study’s findings are available from the corresponding author upon reasonable request. The University of Minnesota—Twin Cities Institutional Review Board approved this research Results 878 patients were evaluated, contributing 2,290 observational units (EHR passage-mRS score pairs) to LLM training. Observational units were distributed across two stages: discharge (n=1,325) and 90 days post-discharge (n=966). This represents 75% (1,325/1,765) of the potential observational units at discharge and 55% (966/1,765) of the potential observational units from the post-discharge period. Figure 1b details the process of EHR passage-mRS score pairs inclusion and exclusion at each time point. The demographics were similar between groups, with a median age of 71 years (IQR 60–81) vs. 70 years (IQR 59–80), ischemic stroke proportions of 84% vs. 87%, and hemorrhagic stroke proportions of 10% vs. 8.2% ( Table 1 ). The median time from hospitalization to discharge was 1 day (IQR 1–3), and from hospitalization to follow-up was 79 days (IQR 56–103). View this table: View inline View popup Table 1. Model training and evaluation data characteristics The two-class model, which combined the mRS scores into two categories (mRS 0-2 vs. mRS 3-6), had an accuracy of 92% and a Cohen’s Kappa of 0.85, and the model’s confusion matrix is shown in Figure 2 . The multi-class model, which included all seven categories of the mRS, achieved an accuracy of 77%, a Cohen’s Kappa of 0.71, and a weighted Kappa of 0.92. Figure 3 represents the confusion matrix for this model and shows that the highest classification accuracy occurred for scores of 0 (90%) and 6 (99%). Misclassification was more common among intermediate scores, particularly score 2, where class accuracy was limited to 25%. Greater variability was observed in the most common words for intermediate mRS scores versus extreme scores, which complicated their classification. This finding is illustrated by Figure 4 , a heatmap of the most common words in each mRS strata. Download figure Open in new tab Figure 2. Confusion Matrix of the binomial classification model as (a) absolute counts and (b) class accuracy Download figure Open in new tab Figure 3. Confusion Matrix of the multiclass classification model as (a) absolute counts and (b) class accuracy Download figure Open in new tab Figure 4. Heatmap of the most common words extracted in EHR passage for each class at (a) discharge and (b) long-term follow-up. Discussion The mRS has become a standard outcome measure in stroke clinical trials due to its ability to capture significant levels of disability while remaining intuitive and less time-intensive to ascertain compared to more detailed metrics 15 , 18 . Automated tools capable of deriving the mRS from EHRs could streamline clinical research and potentially enable large-scale analyses using existing databases that lack mRS outcome data. Our multiclass model demonstrated an accuracy of 77%, marking a significant improvement over previously published machine learning models for mRS determination from the EHR, which reported an accuracy of 59% 14 . Furthermore, our model also had a Cohen’s Kappa of 0.71 and a weighted Kappa of 0.92. The weighted Kappa, which penalizes larger classification errors more heavily than smaller ones, further underscores the model’s performance by indicating that most errors are near-misses rather than substantial misclassifications. In a systematic review of stroke trials, human raters classified mRS with an average agreement accuracy of 73%, unweighted Kappa of 0.62, weighted Kappa of 0.87 6 . This suggests our model performs comparably to human raters and could assist in real-world applications. Our two-class model achieved an accuracy of 92% in differentiating patients who achieved functional independence (mRS score 0-2) from those with functional dependence (mRS score 3-6). This demonstrates that reducing the number of classes can significantly improve model performance, a key consideration depending on application. We observed poorer model performance in the multiclass model, especially when differentiating the mRS score 2, and 3 ( Figure 3 ). The model’s lower class-specific accuracies for intermediate mRS scores illustrate the inherent challenges of assessing mRS. In a study of 7,374 patients, mRS scores of 0–2 showed narrow variability in Longshi scale and Barthel Index scores, whereas mRS scores of 3–4 exhibited much broader variability 19 . This disparity demonstrates that while the mRS score effectively identifies stroke patients with mild disability, it is less capable of differentiating between moderate and severe disability. This known limitation of the mRS contributed to our choice of study design, which included only instances in which score consensus was achieved and excluded cases where EHR notes were ambiguous regarding a patient’s functional status. While this design helped maintain a reliable dataset, we acknowledge that there is likely selection bias due to the exclusion of cases that may be common in clinical practice. Future iterations of LLMs should assess performance across sociodemographic groups, representing an important area for future investigation. Rather than replacing human raters, our model could assist in mRS assessment by confirming initial evaluations. We propose that the model could act as a secondary reviewer flagging uncertain cases for additional human review. By providing the model with passages of EHR text, independent confirmation of mRS scores can occur, using an additional reviewer only for complex cases. This approach could reduce the number of personnel needed for chart review studies while also improving the reproducibility of data collection. With minor prompt engineering, our model could be trained to output class probabilities rather than discrete scores, allowing users to change the threshold of class determination based on the model application. With further advancements, fully automating mRS scoring from unstructured clinical text could integrate real-time outcome metrics into Learning Health Systems. This enables dynamic feedback to guide healthcare delivery and system-level decision-making. Our findings support the continued validation, investigation, and integration of LLMs into medical research and clinical care Conclusions Our study found that a fine-tuned LLM using EHR passages could accurately classify 77% of mRS scores in the multiclass model (scoring mRS 0-6) and 92% of mRS scores in the two-class model (stratifying mRS 0-2 versus mRS 3-6). Utilizing fine-tuned LLMs to determine mRS scores could streamline data analysis for research and clinical applications. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Sources of Funding Luis Silva - NIH National Institute of Neurological Disorders and Stroke StrokeNet fellowship Jeremy Yeung - University of Minnesota Data Science Initiative Seed Grant Halil Ergen - Tubitak 2219 scholarship program Kamakshi Lakshminarayan is supported by K24AG078506 Disclosures The authors declare no conflicts of interest relevant to this study. Acknowledgments The authors would like to thank the Department of Neurology and the School of Public Health at the University of Minnesota for their invaluable efforts in making this research possible. Additionally, we would like to acknowledge the use of automated assistive writing technologies and tools in manuscript and coding development. 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Share Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model Luis Silva , Marcus Milani , Sohum Bindra , Salman Ikramuddin , Megan Tessmer , Kaylee Frederickson , Abhigyan Datta , Halil Ergen , Alex Stangebye , Dawson Cooper , Kompal Kumar , Jeremy Yeung , Kamakshi Lakshminarayan , Christopher D Streib medRxiv 2025.04.30.25326777; doi: https://doi.org/10.1101/2025.04.30.25326777 Share This Article: Copy Citation Tools Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model Luis Silva , Marcus Milani , Sohum Bindra , Salman Ikramuddin , Megan Tessmer , Kaylee Frederickson , Abhigyan Datta , Halil Ergen , Alex Stangebye , Dawson Cooper , Kompal Kumar , Jeremy Yeung , Kamakshi Lakshminarayan , Christopher D Streib medRxiv 2025.04.30.25326777; doi: https://doi.org/10.1101/2025.04.30.25326777 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffd39ba2effe2c5',t:'MTc3OTQ2NzYzNg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();