The Affordable Care Act (ACA), passed in 2010, set forth a framework for healthcare providers to have a vested interest in better patient outcomes and to reduce the Total Cost of Care (TCOC) for patients. A large portion of TCOC comes from patients who make multiple unscheduled hospital visits for the same underlying pathology: a hospital readmission. In this paper, we tackle the difficulty of identifying risk markers for diabetes patients’ hospital readmission. Using data from the Health Facts Database, we use logistic regression and support vector machines to identify the risk that a diabetes patient has of a hospital readmission.
Graham, Ethan; Saxena, Asha; and Kirby, Heather
"Identifying High Risk Patients for Hospital Readmission,"
SMU Data Science Review: Vol. 2
, Article 22.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss1/22
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