The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.

4区 医学
Annals of translational medicine Pub Date : 2025-02-28 Epub Date: 2025-02-25 DOI:10.21037/atm-24-150
Mui Teng Chua, Yuru Boon, Zi Yao Lee, Jian Hao Jaryl Kok, Clement Kee Woon Lim, Nicole Mun Teng Cheung, Lorraine Pei Xian Yong, Win Sen Kuan
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引用次数: 0

Abstract

Background and objective: Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.

Methods: We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: ("artificial intelligence" OR "machine learning" OR "neural networks, computer" OR "deep learning" OR "natural language processing"), AND ("sepsis" OR "septic shock", AND "emergency services" OR "emergency department"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.

Key content and findings: Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.

Conclusions: Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.

人工智能在急诊科败血症中的作用:叙述性回顾。
背景与目的:早期识别和治疗败血症在急诊科(ED)是重要的。传统的预测分析和临床决策规则在识别败血症患者方面缺乏准确性。人工智能(AI)在医疗保健领域越来越普遍,并在脓毒症患者的护理中提供了应用潜力。方法:我们在PubMed、Embase、谷歌Scholar和Scopus数据库中检索了2010年1月1日至2024年6月30日期间发表的关于评估AI在成年ED脓毒症患者中的应用的文献,使用以下搜索词:(“人工智能”或“机器学习”或“神经网络”、“计算机”或“深度学习”或“自然语言处理”),以及(“败血症”或“感染性休克”,以及“紧急服务”或“急诊科”)。独立搜查一式两份,差异由第三名成员裁决。关键内容和发现:与传统模型相比,人工智能可以将生命体征、自由文本输入、实验室测试和心电图等多个变量纳入其中,从而提高诊断性能。机器学习(ML)模型在败血症的诊断和预后方面优于传统评分工具。ML模型能够分析随时间变化的趋势,并在预测死亡率、严重败血症和感染性休克方面显示出实用性。此外,实时ML辅助警报系统在改善抗生素给药时间方面是有效的,ML算法可以将败血症患者区分为不同的表型,以定制管理(特别是液体治疗和重症监护干预),潜在地改善结果。现有的败血症人工智能工具目前缺乏通用性和用户接受度。这是自动化偏差的风险,如果过度依赖发展,临床医生会失去技能。结论:总的来说,人工智能作为一种临床支持工具,在急诊科脓毒症患者的革命性管理方面具有很大的前景。然而,其应用目前仍受到固有局限性的制约。人工智能技术与临床医生投入的平衡整合对于充分发挥其潜力并确保患者获得最佳结果至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
自引率
0.00%
发文量
769
期刊介绍: The Annals of Translational Medicine (Ann Transl Med; ATM; Print ISSN 2305-5839; Online ISSN 2305-5847) is an international, peer-reviewed Open Access journal featuring original and observational investigations in the broad fields of laboratory, clinical, and public health research, aiming to provide practical up-to-date information in significant research from all subspecialties of medicine and to broaden the readers’ vision and horizon from bench to bed and bed to bench. It is published quarterly (April 2013- Dec. 2013), monthly (Jan. 2014 - Feb. 2015), biweekly (March 2015-) and openly distributed worldwide. Annals of Translational Medicine is indexed in PubMed in Sept 2014 and in SCIE in 2018. Specific areas of interest include, but not limited to, multimodality therapy, epidemiology, biomarkers, imaging, biology, pathology, and technical advances related to medicine. Submissions describing preclinical research with potential for application to human disease, and studies describing research obtained from preliminary human experimentation with potential to further the understanding of biological mechanism underlying disease are encouraged. Also warmly welcome are studies describing public health research pertinent to clinic, disease diagnosis and prevention, or healthcare policy.
 With a focus on interdisciplinary academic cooperation, ATM aims to expedite the translation of scientific discovery into new or improved standards of management and health outcomes practice.
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