Using neural attention networks to detect adverse medical events from electronic health records

J Biomed Inform. 2018 Nov;87:118-130. doi: 10.1016/j.jbi.2018.10.002. Epub 2018 Oct 15.


The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality improvement of health services. Recently, with the rapid development of hospital information systems, a large volume of Electronic Health Records (EHRs) have been produced, in which AMEs are regularly documented in a free-text manner. In this study, we are concerned with the problem of AME detection by utilizing a large volume of unstructured EHR data. To address this challenge, we propose a neural attention network-based model to incorporate the contextual information of words into AME detection. Specifically, we develop a context-aware attention mechanism to locate salient words with respect to the target AMEs in patient medical records. And then we combine the proposed context attention mechanism with the deep learning tactic to boost the performance of AME detection. We validate our proposed model on a real clinical dataset that consists of 8845 medical records of patients with cardiovascular diseases. The experimental results show that our proposed model advances state-of-the-art models and achieves competitive performance in terms of AME detection.

Keywords: Adverse medical event; Cardiovascular disease; Deep learning; Electronic health record; Neural attention network.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Area Under Curve
  • Cardiovascular Diseases / diagnosis
  • Cardiovascular Diseases / epidemiology
  • China
  • Databases, Factual
  • Deep Learning*
  • Electronic Health Records / standards*
  • Hemorrhage
  • Hospital Information Systems
  • Hospitals
  • Humans
  • Medical Informatics / methods*
  • Myocardial Ischemia / diagnosis
  • Myocardial Revascularization
  • Neural Networks, Computer*