A natural language processing approach to categorise contributing factors from patient safety event reports.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Azade Tabaie, Srijan Sengupta, Zoe M Pruitt, Allan Fong
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引用次数: 0

Abstract

Objectives: The objective of this study was to explore the use of natural language processing (NLP) algorithm to categorise contributing factors from patient safety event (PSE). Contributing factors are elements in the healthcare process (eg, communication failures) that instigate an event or allow an event to occur. Contributing factors can be used to further investigate why safety events occurred.

Methods: We used 10 years of self-reported PSE reports from a multihospital healthcare system in the USA. Reports were first selected by event date. We calculated χ2 values for each ngram in the bag-of-words then selected N ngrams with the highest χ2 values. Then, PSE reports were filtered to only include the sentences containing the selected ngrams. Such sentences were called information-rich sentences. We compared two feature extraction techniques from free-text data: (1) baseline bag-of-words features and (2) features from information-rich sentences. Three machine learning algorithms were used to categorise five contributing factors representing sociotechnical errors: communication/hand-off failure, technology issue, policy/procedure issue, distractions/interruptions and lapse/slip. We trained 15 binary classifiers (five contributing factors * three machine learning models). The models' performances were evaluated according to the area under the precision-recall curve (AUPRC), precision, recall, and F1-score.

Results: Applying the information-rich sentence selection algorithm boosted the contributing factor categorisation performance. Comparing the AUPRCs, the proposed NLP approach improved the categorisation performance of two and achieved comparable results with baseline in categorising three contributing factors.

Conclusions: Information-rich sentence selection can be incorporated to extract the sentences in free-text event narratives in which the contributing factor information is embedded.

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从患者安全事件报告中对促成因素进行分类的自然语言处理方法。
研究目的本研究旨在探索使用自然语言处理(NLP)算法对患者安全事件(PSE)的诱因进行分类。诱因是指医疗过程中引发事件或导致事件发生的因素(如沟通失败)。诱因可用于进一步调查安全事件发生的原因:我们使用了美国一家多医院医疗系统 10 年来的自我报告 PSE 报告。首先按事件发生日期选择报告。我们计算了词包中每个 ngram 的 χ2 值,然后选出 N 个具有最高 χ2 值的 ngram。然后,对 PSE 报告进行过滤,使其只包含包含所选 ngrams 的句子。这些句子被称为信息丰富的句子。我们比较了自由文本数据中的两种特征提取技术:(1) 基线词袋特征和 (2) 信息丰富句子特征。我们使用了三种机器学习算法对代表社会技术错误的五个促成因素进行分类:沟通/手忙脚乱、技术问题、政策/程序问题、分心/中断和失误/滑倒。我们训练了 15 个二元分类器(五个促成因素 * 三个机器学习模型)。根据精确度-召回曲线下面积(AUPRC)、精确度、召回率和 F1 分数对模型的性能进行了评估:结果:应用信息丰富的句子选择算法提高了诱因分类的性能。比较 AUPRC,建议的 NLP 方法提高了两个因素的分类性能,并在三个因素的分类中取得了与基线相当的结果:结论:可以利用信息丰富的句子选择来提取自由文本事件叙述中包含诱因信息的句子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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