Data driven models in health care management
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Citation
Abstract
The increasing role of Machine Learning (ML) in scientific and biomedical fields has significantly advanced health and medical informatics. ML-based predictive models have shown great potential in identifying critical features and enabling early-stage treatment. This research explores the increasing worldwide shift towards using data-driven models to support decision-making in healthcare.
This work investigates data-driven techniques to predict health-related events and elucidates important predictive variables. Firstly, it identifies key factors in predicting poorly controlled hypertension using demographic and socioeconomic data, revealing that age, race, social determinants of health, mental health, marital status, cigarette use, and gender are predictive of high systolic blood pressure (SBP). Secondly, it develops a personalized hypertension management model that optimizes medication outcomes using Distributionally Robust Optimization (DRO) regularized regression and K-Nearest Neighbors (K-NN) regression, achieving a significant reduction in SBP compared to standard care. Thirdly, it predicts critical COVID-19 treatment outcomes including hospitalization, ICU care, mechanical ventilation, and mortality using both linear and nonlinear classification methods, achieving high predictive accuracy. Lastly, it emphasizes the importance of model interpretability and alignment with medical knowledge to ensure adoption and trust in clinical settings. By introducing the Wasserstein DRO formulation and the Grouped LASSO (GLASSO) algorithm, the research demonstrates enhanced interpretability and credibility, effectively merging model performance with expert knowledge.
Together, these contributions demonstrate the potential of data-driven models to enhance healthcare delivery and patient care by providing a comprehensive approach that balances interpretability, credibility, and expert insight integration.