人工智能在COVID-19预后分层中的应用:述评

Shanghai chest Pub Date : 2021-01-01 DOI:10.21037/shc-21-17
E. Prisciandaro, L. Bertolaccini, L. Spaggiari
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引用次数: 0

摘要

目的:结合人工智能解决方案的新方法已被证明是成功的和有价值的决策支持。本综述的目的是描述新兴的人工智能在COVID-19患者预后分层和分析中的应用。背景:COVID-19已成为突发公共卫生事件,对全球卫生系统产生了惊人的社会和经济影响。至关重要的是要确定罹患严重COVID-19风险最高的患者,从而改善资源分配。方法:在EMBASE(通过Ovid)、MEDLINE(通过PubMed)和Cochrane CENTRAL中对数据库建立日期至2021年1月31日之间发表的英文文章进行系统文献检索。结论:已经设想了几种基于人工智能的方法,以缓解超负荷的卫生系统的压力,并协助临床医生对COVID-19患者进行预后分析。风险评估和分类是必要的:通过确定更可能患有急性疾病的受试者,可能有可能计划更密切的监测和/或更早的治疗干预。因此,人工智能(AI)可以帮助医生根据预后评估调整他们的管理策略,从而提高护理质量。这也将有助于在不允许粗心的供应分配的情况下进行资源分配。人工智能可以帮助医生根据预后评估调整他们的管理策略,从而提高护理质量。©2022 Quantum。版权所有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of artificial intelligence to prognostic stratification of COVID-19: a narrative review
Objective: New approaches incorporating artificial intelligence solutions have proven successful and valuable support for decision-making. The purpose of this review is to describe the emerging artificial intelligence applications to the prognostic stratification and profiling of patients suffering from COVID-19. Background: COVID-19 has become a public health emergency, alarming social and economic impact on healthcare systems worldwide. It is paramount to identify patients at the highest risk of developing severe COVID-19, thus improving resource allocation. Methods: A systematic literature search for articles published in English between the date of database inception and January 31, 2021, was performed in EMBASE (via Ovid), MEDLINE (via PubMed) and Cochrane CENTRAL. Conclusions: Several artificial intelligence-based approaches have been conceived to ease the pressure on the overloaded health system and assist clinicians in the prognostic profiling of COVID-19 patients. Risk assessment and categorisation are essential: By identifying the more likely subjects to suffer from an acute disease, it might be possible to plan a closer monitoring and/or earlier therapeutic intervention. Hence, artificial intelligence (AI) may support physicians in adjusting their management strategy according to the prognostic estimation, resulting in improved quality of care. This would also facilitate resource allocation in a time when careless supply distribution is not allowed. Artificial intelligence may support physicians in adjusting their management strategy according to the prognostic estimation, resulting in improved quality of care. © 2022 Quantum. All rights reserved.
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