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Title page for ETD etd-03202018-104702


Type of Document Master's Thesis
Author Mize, Dara Lee Eckerle
URN etd-03202018-104702
Title A Prediction Model for Disease-Specific 30-Day Readmission Following Hospital Discharge
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Mia Levy Committee Chair
Colin Walsh Committee Member
Shubhada Jagasia Committee Member
Keywords
  • machine learning
  • readmissions
Date of Defense 2018-03-12
Availability unrestricted
Abstract
The Hospital Readmissions Reduction Program (HRRP) permits Centers for Medicare and Medicaid Services (CMS) to reduce reimbursement to hospitals with excess 30-day unplanned readmissions. Diabetes disproportionately impacts the hospitalized patient population, affecting 25-30% of admissions and increases the risk for unplanned readmission. We hypothesized that a readmission risk prediction model for hospitalized patients with type 2 diabetes using machine learning and a diagnosis-specific 30-day readmission outcome will outperform traditional prediction models. We demonstrate that L1 penalized logistic regression and random forest show improved discriminatory performance over LACE, a commonly used logistic regression-based model predicting all-cause readmission. L1 penalized logistic regression is also well-calibrated, efficient and produces interpretable results through feature selection. Random forest was less well-calibrated consistent with its use in other areas of the biomedical literature. In the setting of class imbalance, all of our models suffered from low precision at low thresholds near the outcome prevalence. Random forest precision improved when evaluated at higher thresholds enabling application in a clinical setting. Using an approach that includes a diagnosis-specific outcome enables actionable models for use by disease-specific service lines. Prospective evaluation is needed to assess the validity of this approach and to evaluate for overfitting in the setting of class imbalance.
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