[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125582-en":3,"doc-seo-125582-105":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},125582,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment","Chronic musculoskeletal pain affects a large proportion of people globally and reduces mobility, social participation, work capacity, and quality of life. Interdisciplinary multimodal pain treatment programs can support patients in behavior change and in achieving value-based goals for better pain management. Using 2019–2021 Centre for Integral Rehabilitation data (n=2,364), a multidimensional machine learning framework was developed to predict outcomes across five clinical domains using 13 measures. The resulting stratified prognostic patient profile enables consistent outcome assessment and may support clinician-aided patient selection and goal setting.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10.3389/fpain.2023.1177070  \nEDITED BY  \nAbhinav Kaushik,  \nStanford University, United States  \nREVIEWED BY  \nSayane Shome,  \nStanford University, United States Eleni G. Hapidou,  \nMcMaster University, Canada  \n*CORRESPONDENCE  \nRob J. E. M. Smeets  \n [r.smeets@maastrtichtuniversity.nl](r.smeets@maastrtichtuniversity.nl)  \nRECEIVED 01 March 2023  \nACCEPTED 07 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nZmudzki F and Smeets RJEM (2023) Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.  \nFront. Pain Res. 4:1177070 .  \ndoi: 10.3389/fpain.2023.1177070  \nCOPYRIGHT  \n© 2023 Zmudzki and Smeets. This is an openaccess 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.  \nMachine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment  \nFredrick Zmudzki1,2 and Rob J. E. M. Smeets3,4,5*  \n1Époque Consulting, Sydney, NSW, Australia, 2Social Policy Research Centre, University of New South Wales, Sydney, NSW, Australia, 3Department of Rehabilitation Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Life Sciences and Medicine, Maastricht University, Maastricht, Netherlands, 4CIR Rehabilitation, Eindhoven, Netherlands, 5Pain in Motion International Research Group (PiM), Brussels, Belgium  \nIntroduction: Chronic musculoskeletal pain is a prevalent condition impacting around 20% of people globally; resulting in patients living with pain, fatigue, restricted social and employment capacity, and reduced quality of life. Interdisciplinary multimodal pain treatment programs have been shown to provide positive outcomes by supporting patients modify their behavior and improve pain management through focusing attention on specific patient valued goals rather than fighting pain.  \nMethods: Given the complex nature of chronic pain there is no single clinical measure to assess outcomes from multimodal pain programs. Using Centre for Integral Rehabilitation data from 2019–2021 (n = 2,364), we developed a multidimensional machine learning framework of 13 outcome measures across 5 clinically relevant domains including activity/disability, pain, fatigue, coping and quality of life. Machine learning models for each endpoint were separately trained using the most important 30 of 55 demographic and baseline variables based on minimum redundancy maximum relevance feature selection. Five-fold cross validation identified best performing algorithms which were rerun on deidentified source data to verify prognostic accuracy.  \nResults: Individual algorithm performance ranged from 0 .49 to 0 . 65 AUC reflecting characteristic outcome variation across patients, and unbalanced training data with high positive proportions of up to 86% for some measures. As expected, no single outcome provided a reliable indicator, however the complete set of algorithms established a stratified prognostic patient profile. Patient level validation achieved consistent prognostic assessment of outcomes for 75 .3% of the study group (n = 1,953) . Clinician review of a sample of predicted negative patients (n =81) independently confirmed algorithm accuracy and suggests the prognostic profileis potentially valuable for patient selection and goal setting.  \nDiscussion: These results indicate that although no single algorithm was individually conclusive, the complete stratified profile consistently identified patient outcomes. Our predictive profile provides promising positive contribution for clinicians and patients to assist with personaliz","cbCailI2vCmqvpYE","https://ap.wps.com/l/cbCailI2vCmqvpYE","pdf",5852934,1,18,"English","en",105,"# Introduction\n## Clinical problem and rationale for multimodal interdisciplinary treatment\n# Methods\n## Data source and outcome domains\n## Feature selection and model validation\n# Results\n## Algorithm performance and patient-level prognostic profile\n# Discussion\n## Clinical implications for patient selection and goal setting","[{\"question\":\"Why is interdisciplinary multimodal treatment important for chronic musculoskeletal pain?\",\"answer\":\"It helps patients modify behavior and manage pain by focusing attention on specific value-based goals rather than solely trying to eliminate pain, with positive sustained outcomes even when other modalities have failed.\"},{\"question\":\"How was the machine learning decision support framework built?\",\"answer\":\"It used Centre for Integral Rehabilitation data from 2019–2021 (n=2,364) to train models for 13 outcome measures across five clinically relevant domains, using selected demographic and baseline variables.\"},{\"question\":\"Can clinicians rely on a single predicted outcome?\",\"answer\":\"No single outcome provided a reliable indicator, but the complete set of algorithms formed a stratified prognostic patient profile that consistently assessed outcomes for most participants.\"}]","Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment | 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