一种自然语言处理方法,从患者安全事件报告中分类贡献因素
Azade Tabaie1, Srijan Sengupta2, Zoe M Pruitt3
1Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, District of Columbia, USA azade.tabaie@medstar.net.
BMJ health & care informatics
|May 31, 2023
概括
这项研究使用自然语言处理 (NLP) 来识别患者安全事件 (PSE) 的贡献因素. 一种信息丰富的句子选择方法改善了从医疗保健报告中对这些因素的分类.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 自然语言处理自然语言处理.
- 患者安全 患者安全
背景情况:
- 患者安全事件 (PSE) 需要彻底调查以了解根本原因.
- 识别自由文本报告中的贡献因素对于改善医疗保健质量至关重要.
- 目前用于分析PSE叙述的方法可能耗时,可能错过关键细节.
研究的目的:
- 评估一种自然语言处理 (NLP) 算法,用于对患者安全事件 (PSE) 的贡献因素进行分类.
- 评估信息丰富的句子选择技术的有效性,以从PSE报告中提取相关数据.
- 将基于NLP的特征提取方法与社会技术错误分类的基线方法进行比较.
主要方法:
- 利用来自美国多医院系统的10年自我报告的PSE报告.
- 实现了一个NLP算法,根据n-gram分析进行信息丰富的句子选择.
- 通过使用三种机器学习模型训练和评估15个二元分类器,对五种类型的贡献因素进行分类 (通信/交付,技术,政策/程序,分心/中断,失误/滑落).
- 使用精度回忆曲线 (AUPRC) 下面的面积,精度,回忆和F1得分来衡量性能.
主要成果:
- 信息丰富的句子选择算法显著提高了贡献因素分类的性能.
- 拟议的NLP方法改善了两个因素的分类,并显示了与其他三个因素的基线方法相似的结果.
- 这表明了针对性句子提取在分析复杂的医疗保健事件数据中的价值.
结论:
- 信息丰富的句子选择是从自由文本患者安全事件叙述中提取嵌入式贡献因素信息的有效方法.
- 这种NLP驱动的方法可以简化PSE报告的分析,从而更有效地识别安全问题.
- 这些发现支持将先进的NLP技术整合到患者安全监测系统中.
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