Abstract

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

Publication Date

12-2021

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Linwei Wang

Advisor/Committee Member

Christopher Homan

Advisor/Committee Member

Rui Li

Campus

RIT – Main Campus

Plan Codes

COMPSCI-MS

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