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

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

Journal

Annual review of biomedical engineering
ISSN: 1545-4274
Titre abrégé: Annu Rev Biomed Eng
Pays: United States
ID NLM: 100883581

Informations de publication

Date de publication:
08 06 2023
Historique:
medline: 12 6 2023
pubmed: 1 3 2023
entrez: 28 2 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 36854259
doi: 10.1146/annurev-bioeng-110220-030247
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

131-156

Auteurs

Bjoern M Eskofier (BM)

Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; email: bjoern.eskofier@fau.de.

Jochen Klucken (J)

Digital Medicine Group, Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Belvaux, Luxembourg.
Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg.

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Classifications MeSH