机器学习可以个性化心血管治疗败血症吗?
Finneas J R Catling1,2, Myura Nagendran2,3, Paul Festor3,4
1Institute of Healthcare Engineering, University College London, London, United Kingdom.
Critical care explorations
|May 6, 2024
概括
机器学习可以通过解决患者异质性来个性化败血症治疗. 然而,目前的系统需要更多的临床试验和更好的数据,以便在重症监护中安全有效地使用.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学信息学 生物医学信息学
- 心血管生理学心血管生理学
背景情况:
- 败血症治疗试验通常由于患者异质性而失败,特别是在败血症休克期间的心血管反应中.
- 需要个性化医疗方法来改善败血症复苏的结果.
研究的目的:
- 探索机器学习 (ML) 系统在败血症中个性化心血管复苏的潜力.
- 确定当前的局限性,并提出将ML转化为治疗败血症的临床实践的解决方案.
主要方法:
- 审查关于ML在败血症复苏中的应用现有文献.
- 讨论技术准备,数据要求和临床验证挑战.
- 对临床转化障碍的分析,包括数据质量和模型概括.
主要成果:
- 许多ML"概念验证"存在,但技术准备程度较低.
- 目前的电子健康记录数据缺乏可操作的ML驱动治疗建议的解决方案.
- 关于混,概括性和临床验证,仍然存在重大挑战.
结论:
- 翻译用于败血症复苏的ML需要解决技术障碍和实施障碍.
- 建议包括改善数据质量,使用因果模型,确保安全性和进行临床试验.
- 需要采取协调一致的努力,以弥合ML开发和临床应用在败血症护理中的差距.
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