[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122000-en":3,"doc-seo-122000-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122000,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","A machine learning framework to classify musculoskeletal injury risk groups in military servicemembers","Musculoskeletal injuries are pervasive in military populations, making timely identification and mitigation of risk essential. A survival machine learning framework was developed using self-reported MSKI risk screening data collected during standard unit in-processing, combined with MSKI and demographic information abstracted from electronic health records. Models were trained on 75% of U.S. Army participants and evaluated on 25% across multiple time horizons. The Cox approach achieved the strongest time-dependent discrimination, stratifying servicemembers into risk quartiles with clinically interpretable hazard ratios.","TYPE Original Research PUBLISHED 19 June 2024  \nDOI 10. 3389/frai.2024.1420210  \nOPEN ACCESS  \nEDITED BY  \nTse-Yen Yang,  \nChina Medical University Hospital, Taiwan  \nREVIEWED BY  \nTaChen Chen,  \nChia Nan University of Pharmacy and Science, Taiwan  \nAntonio Sarasa-Cabezuelo, Complutense University of Madrid, Spain  \n*CORRESPONDENCE  \nMatthew B. Bird  \n [matthew.b.bird.civ@health.mil](matthew.b.bird.civ@health.mil)  \nRECEIVED 19 April 2024  \nACCEPTED 27 May 2024  \nPUBLISHED 19 June 2024  \nCITATION  \nBird MB, Roach MH, Nelson RG, Helton MSand Mauntel TC (2024) A machine learning framework to classify musculoskeletal injury risk groups in military service members. Front. Artif. Intell. 7:1420210 .  \ndoi: 10.3389/frai.2024.1420210  \nCOPYRIGHT  \n© 2024 Bird, Roach, Nelson, Helton and Mauntel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning framework to classify musculoskeletal injury risk groups in military servicemembers  \nMatthew B. Bird1,2*, Megan H. Roach1,2,3 , Roberts G. Nelson4 , Matthew S. Helton5 and Timothy C. Mauntel1,2,3  \n1 Extremity Trauma and Amputation Center of Excellence, Defense Health Agency, Falls Church, VA, United States, 2 Department of Clinical Investigations, Womack Army Medical Center, Fort Liberty, NC, United States, 3 Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, United States, 4Artificial Intelligence Integration Center, Army Futures Command, Pittsburgh, PA, United States, 5 U. S. Army, Tripler Army Medical Center, Honolulu, HI, United States  \nBackground: Musculoskeletal injuries (MSKIs) are endemic in military populations. Thus, it is essential to identify and mitigate MSKI risks. Time-to-event machine learning models utilizing self-reported questionnaires or existing data (e. g., electronic health records) may aid in creating e􀀈cient risk screening tools.  \nMethods: A total of 4,222U. S. Army Service members completed a self-report MSKI risk screen as part of their unit’s standard in-processing. Additionally, participants’ MSKI and demographic data were abstracted from electronic health record data. Survival machine learning models (Cox proportional hazard regression (COX), COX with splines, conditional inference trees, and random forest) were deployed to develop a predictive model on the training data (75%; n = 2,963) for MSKI risk over varying time horizons (30, 90, 180, and 365 days) and were evaluated on the testing data (25%; n = 987) . Probability of predicted risk (0.00–1.00) from the final model stratified Service members into quartiles based on MSKI risk.  \nResults: The COX model demonstrated the best model performance over the time horizons. The time-dependent area under the curve ranged from 0 . 73 to 0. 70 at 30 and 180 days. The index prediction accuracy (IPA) was 12% better at 180 days than the IPA of the null model (0 variables) . Within the COX model,“other” race, more self-reported pain items during the movement screens, female gender, and prior MSKI demonstrated the largest hazard ratios. When predicted probability was binned into quartiles, at 180 days, the highest risk bin had an MSKI incidence rate of 2,130 .82 􀀆 171. 15 per 1,000 person-years and incidence rate ratio of 4 . 74 (95% confidence interval: 3 .44, 6 . 54) compared to the lowest risk bin.  \nConclusion: Self-reported questionnaires and existing data can be used to create a machine learning algorithm to identify Service members’MSKI risk profiles. Further research should develop more granular Service member-specific MSKI screening tools and create MSKI risk mitigati","cbCaidlkbcxpcdIJ","https://ap.wps.com/l/cbCaidlkbcxpcdIJ","pdf",2694591,1,13,"English","en",105,"# Background\n# Methods\n## Data sources and participant cohort\n## Survival model development and evaluation\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the need to identify and mitigate musculoskeletal injury (MSKI) risks in military servicemembers using efficient, deployable screening approaches.\"},{\"question\":\"How was the predictive model built and tested?\",\"answer\":\"Self-report MSKI risk screens were combined with electronic health record demographic and MSKI data. Survival machine learning models were trained on 75% of participants and evaluated on the remaining 25% across several time horizons.\"},{\"question\":\"Which model performed best and what did it show?\",\"answer\":\"The Cox model showed the best performance over time horizons, with the strongest discrimination reported for certain intervals. Predicted risk quartiles revealed higher MSKI incidence and incidence rate ratios in the highest-risk bin compared with the lowest-risk bin.\"}]","A machine learning framework to classify musculoskeletal injury risk groups in military servicemembers | PDF",1785808222,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-framework-to-classify-musculoskeletal-injury-risk-groups-in-military-servicemembers","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-framework-to-classify-musculoskeletal-injury-risk-groups-in-military-servicemembers/122000/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study targets the need to identify and mitigate musculoskeletal injury (MSKI) risks in military servicemembers using efficient, deployable screening approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model built and tested?",{"text":80,"@type":76},"Self-report MSKI risk screens were combined with electronic health record demographic and MSKI data. Survival machine learning models were trained on 75% of participants and evaluated on the remaining 25% across several time horizons.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what did it show?",{"text":84,"@type":76},"The Cox model showed the best performance over time horizons, with the strongest discrimination reported for certain intervals. Predicted risk quartiles revealed higher MSKI incidence and incidence rate ratios in the highest-risk bin compared with the lowest-risk bin.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]