机器学习用于急诊部风险分层 (MARS-ED) 研究方案,用于基于机器学习技术预测急诊部31天死亡率的预测模型的实施随机控制试验
Paul M E L van Dam1, William P T M van Doorn2, Floor van Gils3
1Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +, PO Box 5800, Maastricht, 6202 AZ, The Netherlands. paul.van.dam@mumc.nl.
Scandinavian journal of trauma, resuscitation and emergency medicine
|January 23, 2024
概括
本试点研究评估了机器学习 (ML) 模型RISK INDEX的临床影响,用于识别高风险急诊室 (ED) 患者. 该试验旨在通过更好的风险分层来改善患者护理.
科学领域:
- 紧急医疗 紧急医疗
- 临床信息学 临床信息学
- 机器学习应用 机器学习应用
背景情况:
- 对于急诊室 (ED) 患者的现有预测模型往往缺乏现实世界的临床影响验证.
- 机器学习 (ML) 为改善医疗保健环境中的风险预测提供了潜力.
- 评估基于ML的预测工具的临床实用性对于其成功实施至关重要.
研究的目的:
- 进行试点临床试验,评估基于ML的预测模型RISK INDEX对ED患者护理的影响.
- 评估风险指数在预测31天死亡率方面的预后准确性和临床实用性.
- 研究ML模型在临床实践中的实施,以优化患者的治疗结果.
主要方法:
- 一个前性的,随机的,开放的标签,非劣等性试点试验,涉及成年ED患者.
- 参与者随机分配到对照组 (照顾通常) 或干预组 (照顾通常 + 风险指数呈现).
- 风险指数模型使用实验室测试和人口统计数据预测31天死亡风险.
主要成果:
- 风险指数在之前的研究中显示,与内部医学专家相比,风险指数表现优越.
- 该试验将评估风险指数对临床治疗决策的影响程度.
- 将收集和分析有关预后准确性和临床影响的数据.
结论:
- 这项试点试验将提供关于基于ML的预测模型在ED中的临床影响和实施的见解.
- 这些发现将为未来对ML临床预测模型的研究提供信息,并有助于优化患者护理.
- 该研究旨在弥合ML模型性能和现实世界的临床应用之间的差距.
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