Your browser doesn't support javascript.
loading
Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine.
Eskofier, Bjoern M; Klucken, Jochen.
Afiliação
  • 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.
  • Klucken J; Digital Medicine Group, Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Belvaux, Luxembourg.
Annu Rev Biomed Eng ; 25: 131-156, 2023 06 08.
Article em En | MEDLINE | ID: mdl-36854259
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.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina de Precisão Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina de Precisão Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article