Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

no code implementations • 9 May 2024 • Fengyi Gao, Xingyu Zhang, Sonish Sivarajkumar, Parker Denny, Bayan Aldhahwani, Shyam Visweswaran, Ryan Shi, William Hogan, Allyn Bove, Yanshan Wang

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.

ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research

no code implementations • 12 Apr 2023 • David Oniani, Bambang Parmanto, Andi Saptono, Allyn Bove, Janet Freburger, Shyam Visweswaran Nickie Cappella, Brian McLay, Jonathan C. Silverstein, Michael J. Becich, Anthony Delitto, Elizabeth Skidmore, Yanshan Wang

Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Data Visualization

Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study

no code implementations • 22 Mar 2023 • Sonish Sivarajkumar, Fengyi Gao, Parker E. Denny, Bayan M. Aldhahwani, Shyam Visweswaran, Allyn Bove, Yanshan Wang

Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of post-stroke patients treated at the University of Pittsburgh Medical Center.

Language Modelling Large Language Model +1