Role of Artificial Intelligence in Improving Syncope Management

IF 5.8 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
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引用次数: 0

Abstract

Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.
人工智能在改善晕厥管理中的作用。
晕厥在普通人群中很常见,也是急症护理中的常见症状。治疗晕厥患者需要花费大量费用。目前面临的挑战包括将晕厥与模拟晕厥区分开来、识别导致晕厥的严重潜在病症以及当前管理中的巨大差异。虽然存在经过验证的风险工具,尤其是用于短期预后的工具,但其应用并不一致,而且目前的方法并不能满足患者的需求/期望。人工智能(AI)技术,如包括自然语言处理在内的机器学习方法,有可能解决目前晕厥管理中的难题。已发表研究的初步证据表明,人工智能可以准确区分晕厥和其模拟者,并预测短期预后/住院情况。最近,对心电图的人工智能分析表明,在检测严重的心脏结构和功能异常方面大有可为,这有可能改善晕厥治疗。未来的人工智能研究有可能解决目前晕厥治疗中存在的问题。人工智能可以通过访问传统和非传统数据自动实时预测风险。不过,还需要采取措施来缓解已知的问题,如通用性、患者隐私、数据保护和责任等。过去,由于分析方法不完善、缺乏计算能力、难以获得强大的计算系统以及无法获得可靠的高质量数据,人工智能的影响十分有限。现在,除了数据之外的所有障碍都已迎刃而解。如果医疗保健系统能够满足人工智能对大规模、强大、准确和可靠数据的要求,那么人工智能将实现其改变晕厥护理的承诺。
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
Canadian Journal of Cardiology
Canadian Journal of Cardiology 医学-心血管系统
CiteScore
9.20
自引率
8.10%
发文量
546
审稿时长
32 days
期刊介绍: The Canadian Journal of Cardiology (CJC) is the official journal of the Canadian Cardiovascular Society (CCS). The CJC is a vehicle for the international dissemination of new knowledge in cardiology and cardiovascular science, particularly serving as the major venue for Canadian cardiovascular medicine.
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