健康恶化的预测模型:了解个性化医疗的疾病途径。

IF 12.8 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL
Bjoern M Eskofier, Jochen Klucken
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)方法目前广泛应用于医学和医疗保健领域。PubMed搜索结果显示,仅在2018年至2022年期间,就有超过10万篇关于这些主题的文章发表。尽管最近对医学中人工智能和机器学习的各个子领域进行了一些综述,但我们还没有看到关于这些方法在个性化疾病途径中用于纵向分析和预测个体患者健康状况的全面综述。本文旨在填补这一空白。在概述了该领域使用的人工智能和机器学习方法以及该类型模型的具体医学应用之后,本文讨论了当前研究的优势和局限性,并展望了该领域未来的研究方向。我们的目标是使有兴趣的读者获得目前可用的研究的详细印象,并相应地围绕健康状况恶化的预测模型计划未来的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine.

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.

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来源期刊
Annual Review of Biomedical Engineering
Annual Review of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
18.80
自引率
0.00%
发文量
14
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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