{"paper_id":"23c73eb0-5473-46b2-bced-a8320f4d9466","body_text":"Development of an Early Prediction Model for Patients with Pressure Injury: Based on Explainable Machine Learning Methods | 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 Research Article Development of an Early Prediction Model for Patients with Pressure Injury: Based on Explainable Machine Learning Methods Longcha Liu, Zhi Chen, Beilei Huang, Danwen Zhuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8939716/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Aim: This study aims to develop an interpretable machine learning model for the early prediction of hospital-acquired pressure injuries (PIs). Design: Retrospective cohort study. Methods: A retrospective study design was employed using electronic health record (EHR) data from hospitalized patients to predict pressure injuries (PIs). The dataset was randomly divided into a training set (70%) and an independent test set (30%). Five machine learning algorithms were developed and compared: eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Naive Bayes (NB), Multilayer Perceptron (MLP), and Random Forest (RF). SHAP (SHapley Additive exPlanations) was applied to interpret the best-performing model. Results: A total of 1524 patients were included, with a pressure injury (PI) incidence of 9.78% (149/1524). The XGBoost model achieved the highest predictive performance, yielding an area under the curve (AUC) of 0.910. In contrast, the Naive Bayes model demonstrated limited generalizability (AUC = 0.841) and relatively poor predictive accuracy. SHAP analysis identified the top 15 predictors of PIs, among which the use of sedative–analgesic drugs, albumin level, and prothrombin time were the most highly predictive. Conclusions: The study successfully developed a machine learning model that enhances the prediction of pressure injuries (PIs) in hospitalized patients. The model, combined with SHAP-derived interpretability, may facilitate early interventions and ultimately reduce the incidence of pressure injuries. pressure injury model machine learning XGBoost SHAP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 26 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8939716\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":610080525,\"identity\":\"eff1ae7a-1652-42b8-a431-30704cdd925c\",\"order_by\":0,\"name\":\"Longcha Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Longcha\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":610080527,\"identity\":\"a3503be4-0971-44f8-9a2e-a9ad14f30608\",\"order_by\":1,\"name\":\"Zhi Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhi\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":610080528,\"identity\":\"c8cc393c-2208-46a1-b08d-07f3327711c7\",\"order_by\":2,\"name\":\"Beilei Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Beilei\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":610080529,\"identity\":\"7cf3c94c-6593-45f4-8e5f-951fa3521be8\",\"order_by\":3,\"name\":\"Danwen Zhuang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACxvbmww8SKiSY+YnWwtxzLM3gwRkLdskGYrWwz8gxkHzYUsFvcIBYLbw9BwwMEhskpI2PJ29g+FGxjbAWyfaGhAeJOySMzc48K2DsOXObsBbDngMHDBLPSCSb3cgxYGZsI0KL/Q2gqxLbJOo3zyBWC+OMZAaQFmYDCaK19BxjM0g4I8EsAfTLQaL8wtje//nhj4o6Zv725I0PflQQoQUJJBAfNQgtpOoYBaNgFIyCEQIA+oZBjCldgrEAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Danwen\",\"middleName\":\"\",\"lastName\":\"Zhuang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-22 14:24:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8939716/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8939716/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":105564603,\"identity\":\"3fbb1ba3-b514-42e1-a8dd-df8613128258\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 12:50:11\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":604560,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"DevelopmentofanEarlyPredictionModel.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8939716/v1_covered_87ffb1e1-442c-4664-a41b-bba8d963865b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Development of an Early Prediction Model for Patients with Pressure Injury: Based on Explainable Machine Learning Methods\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-informatics-and-decision-making\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"midm\",\"sideBox\":\"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/midm/default.aspx\",\"title\":\"BMC Medical Informatics and Decision Making\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"pressure injury, model, machine learning, XGBoost, SHAP\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8939716/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8939716/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eAim: \\u003c/strong\\u003eThis study aims to develop an interpretable machine learning model for the early prediction of hospital-acquired pressure injuries (PIs).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDesign:\\u003c/strong\\u003eRetrospective cohort study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eA retrospective study design was employed using electronic health record (EHR) data from hospitalized patients to predict pressure injuries (PIs). The dataset was randomly divided into a training set (70%) and an independent test set (30%). Five machine learning algorithms were developed and compared: eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Naive Bayes (NB), Multilayer Perceptron (MLP), and Random Forest (RF). SHAP (SHapley Additive exPlanations) was applied to interpret the best-performing model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003eA total of 1524 patients were included, with a pressure injury (PI) incidence of 9.78% (149/1524). The XGBoost model achieved the highest predictive performance, yielding an area under the curve (AUC) of 0.910. In contrast, the Naive Bayes model demonstrated limited generalizability (AUC = 0.841) and relatively poor predictive accuracy. SHAP analysis identified the top 15 predictors of PIs, among which the use of sedative–analgesic drugs, albumin level, and prothrombin time were the most highly predictive.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions: \\u003c/strong\\u003eThe study successfully developed a machine learning model that enhances the prediction of pressure injuries (PIs) in hospitalized patients. The model, combined with SHAP-derived interpretability, may facilitate early interventions and ultimately reduce the incidence of pressure injuries.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Development of an Early Prediction Model for Patients with Pressure Injury: Based on Explainable Machine Learning Methods\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-23 18:45:27\",\"doi\":\"10.21203/rs.3.rs-8939716/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-02T08:52:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"249677343968769916281543462958947938216\",\"date\":\"2026-04-02T08:48:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-19T08:35:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-16T10:22:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-02-26T07:02:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-25T14:53:57+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Medical Informatics and Decision Making\",\"date\":\"2026-02-25T14:48:19+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-informatics-and-decision-making\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"midm\",\"sideBox\":\"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/midm/default.aspx\",\"title\":\"BMC Medical Informatics and Decision Making\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"38ef0e57-1cf8-450d-8ceb-e550b1f2ed89\",\"owner\":[],\"postedDate\":\"March 23rd, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-23T18:45:27+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-23 18:45:27\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8939716\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8939716\",\"identity\":\"rs-8939716\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}