Presentation
From Petabytes to Predictions: Harnessing Large-Scale NeuroBlu Mental Health Data and ML To Mitigate Medication Non-Adherence
DescriptionMedication 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.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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