{"title":"Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine.","authors":"Bjoern M Eskofier, Jochen Klucken","doi":"10.1146/annurev-bioeng-110220-030247","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50757,"journal":{"name":"Annual Review of Biomedical Engineering","volume":"25 ","pages":"131-156"},"PeriodicalIF":12.8000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1146/annurev-bioeng-110220-030247","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/2/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Since 1999, the Annual Review of Biomedical Engineering has been capturing major advancements in the expansive realm of biomedical engineering. Encompassing biomechanics, biomaterials, computational genomics and proteomics, tissue engineering, biomonitoring, healthcare engineering, drug delivery, bioelectrical engineering, biochemical engineering, and biomedical imaging, the journal remains a vital resource. The current volume has transitioned from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license.
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