Machine Learning for Clinical Outcome Prediction

IEEE Rev Biomed Eng. 2021:14:116-126. doi: 10.1109/RBME.2020.3007816. Epub 2021 Jan 22.

Abstract

Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Big Data
  • Decision Making, Computer-Assisted*
  • Decision Support Techniques*
  • Electronic Health Records*
  • Humans
  • Machine Learning*
  • Treatment Outcome