Fine-grained Modeling for Clinical Decisions via Machine Learning
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Cui, Jiaming
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Abstract
Public health challenges threaten people’s lives and place a high burden on our healthcare system. For example, for infectious diseases, COVID-19 has led to 775 million cases and 7 million deaths worldwide as of July 2024, making it one of the largest public health crises in human history. Healthcare-associated infections (HAIs), such as Methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (C. diff), infect approximately 3% of hospitalized patients in the United States every year, resulting in more than 2.8 million cases and 35,000 deaths annually. These challenges force us to make critical clinical decisions in context of public health, such as releasing less severe patients to admit those in greater danger. In turn, such decisions may also lead to more infections in the community, and influence the hospital back. However, it is challenging to use existing epidemiological models to guide such decision-making. The spread pathways of infectious diseases are much more complicated in hospital settings when they interact with healthcare systems, and existing models cannot capture all these pathways effectively to make informed decisions. Additionally, these models also cannot digest the rich clinical datasets that provide a large amount of patient-level data, restricting them from making accurate, fine-grained decisions.
To tackle this, in this dissertation, we propose novel machine learning algorithms that can utilize these datasets to help design more detailed, fine-grained epidemiological models for more accurate infectious disease surveillance and control practices. Specifically, in Part I, we will show how such environmental factor-mediated models could better reconstruct the spread pathway in hospitals for infectious disease surveillance and achieve more cost-efficient contact precaution policies for clinical control. In Part II, we will propose new ML algorithms to better calibrate epidemiological models to learn more accurate model parameters. Moreover, we also designed new frameworks to integrate neural networks and epidemiological models simultaneously, which allows us to incorporate electronic health record (EHR) data to give patient-level predictions. Experimental results on real-world clinical datasets from large hospital systems demonstrate that our models and frameworks lead to more effective and efficient decision-making, thereby better bridging public health with clinical decisions.
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Date
2024-07-26
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Dissertation