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 stateof- 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)
JournalIEEE Reviews in Biomedical Engineering
DOIs
StateAccepted/In press - 2020

Keywords

  • Adaptation models
  • Biological system modeling
  • Data models
  • Feature extraction
  • Hidden Markov models
  • Hospitals
  • Predictive models

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Machine Learning for Clinical Outcome Prediction'. Together they form a unique fingerprint.

Cite this