The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: ACM Student Research Competition, Undergraduate
Author: Alyson Collins (University of Southern Indiana), Cathy Sandoval (University of Southern Indiana), Maya Seshan (University of Southern Indiana), Srishti Srivastava (University of Southern Indiana), Josh McWilliams (University of Southern Indiana)
Supervisor: Srishti Srivastava (University of Southern Indiana)
Abstract: Medication non-adherence is a major public health issue, especially within the behavioral health domain, with traditional measurement methods often being unreliable. This study uses a machine learning approach to predict medication adherence in a large cohort of over 446,000 patients with major depressive disorder, filtered out of a very large-scale dataset containing over 36 million patient records, leveraging de-identified electronic health record data. Our XGBoost model achieved 88% accuracy and an ROC-AUC of 0.94, demonstrating strong predictive performance. Crucially, the use of SHAP provided clinical interpretability, identifying key drivers of adherence, primarily from prescription data. This research highlights the potential of large-scale data and machine learning to enable targeted interventions, improving patient care and reducing healthcare costs.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF