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一个实时自动机器学习算法用于预测创伤患者的死亡率:调查称它已经准备好了

Caroline Park¹, Sandra E Loza-Avalos¹, Jalen Harvey¹

¹Division of Burns, Trauma and Acute Care Surgery, Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.

The American surgeon

|October 17, 2023

概括

这项研究引入了帕克兰创伤死亡率指数 (PTIM),这是一个实时的人工智能工具,用于预测创伤护理中的患者死亡率. PTIM帮助提供者做出决策,并提高死亡率评估的准确性.

科学领域:

创伤外科手术创伤外科
人工智能在医学中的应用
预测分析是一种预测分析.

背景情况:

当前医疗保健中的AI预测模型往往是回顾性的,不适合实时临床决策.
帕克兰创伤死亡率指数 (PTIM) 是一种新的,经过验证的死亡风险计算器,旨在实时使用.
PTIM嵌入电子健康记录 (EHR) 中,以提供高准确度的每小时死亡率预测.

研究的目的:

评估PTIM工具在实时创伤护理环境中的实施和临床实用性.
评估PTIM如何影响外科提供者对患者治疗和手术干预的决策.
确定PTIM是否提高创伤患者死亡风险评估的效率和准确性.

主要方法:

一项基于调查的前性研究,在一级创伤中心进行.
一项匿名调查分发给外科提供者,以收集关于PTIM实施的反.
PTIM使用23个变量进行计算,包括格拉斯哥昏迷评分 (GCS),生命体征和实验室数据.

主要成果:

在40名受访供应商中,有35家供应商表示在决策过程中使用PTIM.
大多数提供者发现PTIM协助治疗决策 (77%) 和手术干预时间 (66%).
提供者一致认为,PTIM能够很好地融入临床工作流程,并提高死亡率评估的效率 (分别为61%和58%).

结论:

PTIM代表了显著的进步,因为它可能是创伤患者的第一个实时,自动化死亡率预测算法.
研究结果表明,PTIM有助于临床决策,优化干预时间,提高死亡率评估的准确性.
需要进一步的研究来评估PTIM对患者短期和长期结果的影响.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.创伤的死亡率和死亡率.

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