Date of Award

8-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Industrial Engineering

Committee Chair/Advisor

Hamed Rahimian

Committee Member

Amin Khademi

Committee Member

Qi Luo

Abstract

In modern healthcare, the convergence of data-driven methodologies and decision making frameworks has become imperative for enhancing patient-care, operational efficiency, and resource allocation. This thesis explores the synergistic integration of two powerful paradigms: machine learning (ML) and Markov decision processes (MDPs), to address key challenges in healthcare management. Chapter 2 showcases the integration of ML techniques for predicting laboratory results using Electronic Medical Records (EMR) data. By harnessing the information embedded within EMRs, ML models offer predictive insights into patient outcomes, facilitating early detection of health risks and enabling personalized interventions. Various ML algorithms suitable for handling EMR data are discussed, alongside challenges related to data preprocessing and model interpretability. Furthermore, recent advancements in deep learning architectures are explored, showcasing their efficacy in longitudinal predictions and clinical decision support. Chapter 3 shifts focus to MDP-based policy analysis, emphasizing its utility in optimizing healthcare decision-making processes. Fundamental concepts of MDPs, algorithms for solving them, and extensions to accommodate real-world complexities are examined. Through implementations and numerical experiments, the chapter illustrates the practical implications of integrative analytics in healthcare settings, highlighting their potential to revolutionize patient-care, resource allocation, and policy development.

Available for download on Sunday, August 31, 2025

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