Towards robust clinical predictive modeling with heterogeneous electronic health record data
Wu, Zhenbang
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Permalink
https://hdl.handle.net/2142/125712
Description
Title
Towards robust clinical predictive modeling with heterogeneous electronic health record data
Author(s)
Wu, Zhenbang
Issue Date
2024-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Sun, Jimeng
Department of Study
Siebel Computing &DataScience
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
deep learning for healthcare
clinical predictive modeling
electronic health records
Abstract
With the widespread adoption of Electronic Health Record (EHR) systems, there has been increasing interest in leveraging deep learning for clinical predictive modeling. However, existing models typically assume a uniform feature and label space. In contrast, different hospitals often use varied EHR systems with unique schemas (i.e., feature space). Additionally, the clinical tasks (i.e., label space) can change dynamically. In this work, we present two methods to address discrepancies in feature and label spaces across different healthcare settings. AutoMap is designed to enable the deployment of clinical predictive models across hospitals with diverse medical coding systems. It automatically aligns medical codes across different EHR systems via ontology-level alignment and code-level refinement. EDGE is designed to recommend newly developed drugs, which often lack extensive historical prescription data. By formulating new drug recommendation as a few-shot learning problem, it employs a drug-dependent multi-phenotype few-shot learner to quickly adapt to new drugs. We validate both methods using real-world EHR datasets from MIMIC-III, MIMIC-IV, eICU, and Claims databases. Our results demonstrate their effectiveness in addressing the challenges posed by unmatched feature and label spaces in clinical predictive modeling.
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