一种机器学习方法与人类-人工智能合作,用于自动分类患者安全事件报告:算法开发和验证研究.
Hongbo Chen1, Eldan Cohen1, Dulaney Wilson2
1Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada.
JMIR human factors
|January 25, 2024
概括
使用上下文文本表示的机器学习分类器显著提高了患者安全事件 (PSE) 报告分类准确性. 一个集成的接口增强了人 - 人工智能合作,以更有效地识别风险和预防患者伤害.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 患者安全 患者安全
背景情况:
- 患者安全事件 (PSE) 报告对于监测医院事件至关重要,但由于数量和不一致性,它们面临分类挑战.
- 基于变压器的语言模型为更精确的PSE报告分类提供先进的上下文文本表示.
- 将机器学习 (ML) 与人类专业知识相结合,需要能够有效解释人类-AI协作.
研究的目的:
- 调查经过背景文本表示训练的ML分类器的有效性,以便自动分类PSE报告.
- 介绍一个接口,将ML分类器与可解释性技术集成在一起,以促进人类-人工智能在PSE报告分类中的合作.
主要方法:
- 利用了来自美国东南部学术医院产科病房的861个PSE报告.
- 培训和评估ML分类器,使用PSE报告的静态和上下文文本表示.
- 采用局部可解释模型-不可知解释 (LIME) 技术进行预测逻辑,并设计了一个集成的报告接口.
主要成果:
- 性能最好的上下文表示分类器达到75.4%的准确性,超过了静态表示分类器 (66.7%).
- 开发了一个PSE报告界面,推顶级事件分类,并提供解释以帮助用户选择.
- LIME分析揭示了分类器偶尔依赖任意单词,强调了需要人类监督的必要性.
结论:
- 语境文本表示显著提高基于ML的PSE报告分类准确性.
- 开发的接口支持人 - 人工智能协作,改善患者安全的决策.
- 这项研究使得更有效的风险识别和及时干预能够防止患者受到伤害.
关键词:
在 LIME 时代,事故 事故 事故 事故 事故事故发生事故事故发生事故人工智能的人工智能是人工智能.黑盒子是一个黑盒子.这是分类分类的分类.分类器分类器是分类器.合作 合作 合作 合作设计 设计 设计 设计文件 文件是指一个文件.文件 文档 文档 文件 文档文件是文件,文件是文件.可以解释性的解释性.可以解释的可解释.人类-人工智能人与人工智能的协作人类-计算机关系人与机器的关系事件报告 事件报告接口 接口 接口 接口 接口界面设计 界面设计 界面设计可以解释的可解释.机器学习是机器学习.患者安全 患者安全预测 预测 预测 预测预测 预测 预测 预测预测 预测 预测 预测预测性 预测性 预测性报告报告报告报告报告.报告报告报告报告报告报告报告在安全方面,安全是安全的.文本 文本 文本文本是文本,文本是文本.文本性 文本性 文本性
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