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
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