Machine Learning for Clinical Outcome Prediction

Farah Shamout, Tingting Zhu, David A. Clifton

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number9134853
Pages (from-to)116-126
Number of pages11
JournalIEEE Reviews in Biomedical Engineering
Volume14
DOIs
StatePublished - 2021

Keywords

  • Learning (artificial intelligence)
  • big data applications
  • decision support systems
  • electronic medical records
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering

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