Towards robust clinical predictive modeling with heterogeneous electronic health record data

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Wu, Zhenbang

Contributors dc:contributor

• Sun, Jimeng

• Deep Learning For Healthcare
• Clinical Predictive Modeling
• Electronic Health Records

Statement dc:rights

• Copyright 2024 Zhenbang Wu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125712

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125712