[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120102-en":3,"doc-seo-120102-105":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},120102,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning - Research report","This study uses statistical analysis and machine learning to evaluate whether rehabilitation exercises can improve post-stroke functional abilities and to forecast their improvement trajectory. Rehabilitation exercise content and demographic factors are drawn from unstructured electronic health record (EHR) data and free-text procedure notes. Focusing on basic mobility and applied cognitive domains, the work aims to identify exercise types that drive functional gains within early and later recovery periods.","Precision Rehabilitation for Patients Post-Stroke based on Electronic Health  \nRecords and Machine Learning  \nFengyi Gao, MS1, Xingyu Zhang, PhD2, Sonish Sivarajkumar, BS3, Parker E. Denny, DPT4,  \nBayan M. Aldhahwani, PT, MS4,5, Shyam Visweswaran, MD, PhD3,6, Ryan Shi, PhD7, William Hogan, MD, MS8, Allyn Bove, DPT, PhD4, Yanshan Wang, PhD1,3,6,8,10  \n1Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA; 2Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA; 3Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA; 4Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA; 5Department of Medical Rehabilitation Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; 6Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA;7Department of Computer Science, University of Pittsburgh Medical Center, Pittsburgh, PA; 8Data Science Institute, Medical College of Wisconsin, Milwaukee, WI; 9Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA; 10Hillman Cancer Center, University of Pittsburgh Medical  \nCenter, Pittsburgh, PA;  \nAbstract  \nObjective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercisesand demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains.  \nData sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation Datamart With Informatics iNfrastructure for Research (ReDWINE) .  \nMethods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability.  \nResults Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substantial early-stage improvement (p\u003C0.001, OR=7.81, 95% CI 2.21-30.30) . In the BM domain, significant late-stage improvements were noted with EYES CLOSED (p\u003C0.05, OR=2.06, 95% CI 1.08-3.94) and GAIT exercises (p\u003C0.01, OR=2.38, 95% CI 1.25-4.56) . The Random Forest model achieved the best performance in predicting functional outcomes.  \nConclusion In this study, we identified three rehabilitation exercises that significantly contributed to patient post-stroke functional ability improvement in the first two months. Additionally, the successful application of a machine learning model to predict patient-specific functional outcomes underscores the potential for precision rehabilitation.  \nKeywords Precision rehabilitation, Natural language processing, ","cbCairattAUGsEIi","https://ap.wps.com/l/cbCairattAUGsEIi","pdf",1777528,1,21,"English","en",105,"# Abstract\n## Objective\n## Data sources\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the primary goal of the precision rehabilitation study?\",\"answer\":\"To determine whether specific rehabilitation exercises improve post-stroke functional abilities and to predict the degree of improvement in basic mobility and applied cognitive domains.\"},{\"question\":\"How are rehabilitation exercises and functional scores extracted from the data?\",\"answer\":\"Rehabilitation exercise information is extracted using an existing NLP approach, while AM-PAC (basic mobility and applied cognitive) scores are obtained from procedure notes via a rule-based NLP algorithm.\"},{\"question\":\"Which machine learning model performed best for predicting functional outcomes?\",\"answer\":\"The Random Forest model achieved the best performance in predicting functional outcomes.\"}]","Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning - 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