Augmenting electronic health records for adverse event detection.

Gün Kaynar¹, Zhaoyi You¹, Richard D Boyce², Takahiro Yakoh³, Carl Kingsford⁴

¹Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

²Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

³Department of System Design Engineering, Keio University, Yokohama, Japan.

⁴Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address: carlk@cs.cmu.edu.

PubMed DOI

Abstract

OBJECTIVE: Adverse events (AEs) resulting from medical interventions are significant contributors to patient morbidity, mortality, and healthcare costs. Detection of these events using electronic health records (EHRs) can facilitate timely clinical interventions. However, effective detection remains challenging due to severe class imbalance, missing labels, and the complexity of EHR records. Class…

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