This thesis research investigates the prediction of readmission risk in heart failure patients using their electronic health record (EHR) data from previous hospitalizations. We examine three primary questions. First, we study the use of attention mechanism in readmission prediction model based on long short-term memory(LSTM) networks and investigate the interpretability it offers regarding the importance of critical time during the visit in readmission prediction. Second given that, generally dataset is curated by combining data from multiple hospitals we investigate model generalization across multiple sites. Finally since in real life scenario model will be trained on past data and used to predict future readmission events, we further investigate model generalization across time. Along with those things, model performance across different endpoints will be studied.
Library of Congress Subject Headings
Heart failure--Patients--Rehabilitation--Quality control--Data processing; Medical records--Data processing; Hospital care--Quality control--Data processing
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Suryawanshi, Pradumna, "Predicting risk of readmission in heart failure patients using electronic health records" (2021). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus