1. Current Year Programs
URMC – Wang, Predicting Adverse Outcomes due to Polypharmacy in Older Adults
May 5, 2025
This project investigated whether patterns in medication use among older adults could help predict rehospitalization after home healthcare. Using data from over 6,800 patients, we analyzed active medications, diagnoses, and patient characteristics, mapping them to standardized codes like RxNorm and PhecodeX. We applied clustering techniques and machine learning models, including XGBoost and BERT-based text embeddings, to identify potential risk factors. Although some variables—like hyper-polypharmacy, reduced physical function (ADL), and certain medication classes—were associated with increased risk, no clear or consistent clusters emerged as highly predictive. Our best-performing model achieved 96% accuracy and 97% ROC-AUC, reinforcing the value of advanced methods but also underscoring the need for individualized deprescribing strategies in geriatric care.
