Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Bjoern M. Eskofier*, Jochen Klucken

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Original languageEnglish
Pages (from-to)131-156
Number of pages26
JournalAnnual Review of Biomedical Engineering
Volume25
Early online date28 Feb 2022
DOIs
Publication statusPublished - 8 Jun 2023

Keywords

  • artificial intelligence
  • digital health pathways
  • disease trajectories
  • machine learning
  • personalized medicine
  • time series

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