Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine
Eskofier B, Klucken J (2023)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2023
Journal
Publisher: Annual Reviews Inc.
Series: Annual Review of Biomedical Engineering
Book Volume: 25
Pages Range: 131-156
DOI: 10.1146/annurev-bioeng-110220-030247
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.
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Luxembourg (LU)How to cite
APA:
Eskofier, B., & Klucken, J. (2023). Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine. In (pp. 131-156). Annual Reviews Inc..
MLA:
Eskofier, Björn, and Jochen Klucken. "Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine." Annual Reviews Inc., 2023. 131-156.
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