TY - JOUR
T1 - Predictive Models for Health Deterioration
T2 - Understanding Disease Pathways for Personalized Medicine
AU - Eskofier, Bjoern M.
AU - Klucken, Jochen
N1 - acknowledgments
The authors extend their thanks to Fatemeh Salehi, Hasan Nazim Bicer, and Ünal Ege Gaznepoglu for helpful literature research during preparation of this review, and to Dario Zanca, Dimitrios Fotiadis, Leo Schwinn, Martha Gray, Paolo Bonato, Patricia Martins Conde, Robert Richer, and Stefano Sapienza for many helpful discussions on and around the topics it discusses. B.M.E. also gratefully acknowledges the generous support of the German Research Foundation (DFG) within the context of the Heisenberg Program (Project-ID ES 434/8-1) and the Sonderforschungsbereich 1483 EmpkinS (Project-ID 442419336). J.K. acknowledges the FNR-PEARL funding granted by the Luxembourg Research Foundation (dHealthPD).
PY - 2023/6/8
Y1 - 2023/6/8
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - digital health pathways
KW - disease trajectories
KW - machine learning
KW - personalized medicine
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85163301905&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/36854259
U2 - 10.1146/annurev-bioeng-110220-030247
DO - 10.1146/annurev-bioeng-110220-030247
M3 - Review article
C2 - 36854259
SN - 1523-9829
VL - 25
SP - 131
EP - 156
JO - Annual Review of Biomedical Engineering
JF - Annual Review of Biomedical Engineering
ER -