Abstract

In the context of heart failure, a leading cause of hospitalization in the United States, approximately 15\% of patients discharged within 30 days face readmission, contributing to escalated healthcare costs and compromised clinical outcomes. This study leverages a wealth of personal information extracted from Emergency Health Records (EHRs) spanning two decades. This research aims to enhance the predictive capabilities of re-hospitalization for cardiac patients by creating a two-stage LSTM model while incorporating features into regression and time series datasets. A critical aspect of this study involves ensuring the integration of all relevant features across both datasets. Including constant data elements, such as demographics and cohort-level statistics, alongside time series data in the model is deemed suboptimal. Consequently, the time series model accommodates two distinct feature sets—one dedicated to time series information and the other to constant data. This work demonstrates that the utilization of time series modeling significantly improves predictive outcomes. Leveraging Recurrent Neural Networks (RNNs) in the form of an LSTM model, the research emphasizes the superiority of this approach in enhancing re-hospitalization prediction accuracy for cardiac patients.

Library of Congress Subject Headings

Heart failure--Patients--Health and hygiene--Prediction; Hospitals--Admission and discharge--Data processing; Neural networks (Computer science); Machine learning; Time-series analysis

Publication Date

12-2023

Document Type

Thesis

Student Type

Graduate

College

Golisano College of Computing and Information Sciences

Advisor

Linwei Wang

Advisor/Committee Member

Rui Li

Advisor/Committee Member

Qi Yu

Campus

RIT – Main Campus

Plan Codes

COMPSCI-MS

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