Enhancing Medical Decision Support Systems for Sepsis Patients in the ICU: Real-Time Detection and Algorithmic Bias Mitigation
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Smith, Jeffrey
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Abstract
The research outlined in this dissertation is comprised of machine learning (ML) and statistical techniques applied to a range of healthcare challenges. Implementing machine learning methodologies in the context of patient electronic health record (EHR) data involves navigating several complex factors. These include managing the unique structure of continuous physiological data and identifying patient health conditions in the absence of standardized definitions, all while being mindful of potential clinical biases impacting model outcomes. This work aims to address these intricate problems using interpretable ML and statistical techniques. The primary objective of this research is to enhance the fairness, transparency, and efficacy of medical-AI models.
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2024-12-06
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Text
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Dissertation