An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling
An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling
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DOI：
10.3414/me15-01-0010
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发表时间：
2015-01-01
影响因子：
1.7
通讯作者：
Ratwani, R.
中科院分区：
文献类型：
--
作者：
Fong, A.;Ratwani, R.
目的：如果进行适当的分析和利用，患者安全事件数据存储库有可能显著提高安全性。这些安全事件报告通常由结构化数据（例如一般事件类型类别）和非结构化数据（例如事件的自由文本描述）两者组成。分析这些数据，特别是丰富的自由文本叙述，可能具有挑战性，尤其是对于数以万计的报告。为了克服资源密集型的人工审查过程中的自由文本的描述，我们证明了使用无监督的自然语言处理approach.Methods的有效性：一个无监督的自然语言处理技术，称为主题建模，被施加到一个大型的存储库的患者安全事件数据，以确定主题，或主题，从自由文本描述的数据。熵的措施被用来评估和比较这些主题的一般事件类型的类别，最初分配的事件reporter. Results：熵的措施表明，一些主题产生的无监督建模方法与临床一般事件类型的类别，最初选择的个人进入报告。重要的是，出现了一些最初没有发现的新的潜在主题。新的主题提供了额外的见解，否则不容易被detected.Conclusion患者安全事件数据：主题建模方法提供了一种方法，以确定主题或主题，可能不会立即明显，并有可能允许自动重新分类的事件是模糊的事件记者分类。
Objective: Patient safety event data repositories have the potential to dramatically improve safety if analyzed and leveraged appropriately. These safety event reports often consist of both structured data, such as general event type categories, and unstructured data, such as free text descriptions of the event. Analyzing these data, particularly the rich free text narratives, can be challenging, especially with tens of thousands of reports. To overcome the resource intensive manual review process of the free text descriptions, we demonstrate the effectiveness of using an unsupervised natural language processing approach.Methods: An unsupervised natural language processing technique, called topic modeling, was applied to a large repository of patient safety event data to identify topics, or themes, from the free text descriptions of the data. Entropy measures were used to evaluate and compare these topics to the general event type categories that were originally assigned by the event reporter.Results: Measures of entropy demonstrated that some topics generated from the unsupervised modeling approach aligned with the clinical general event type categories that were originally selected by the individual entering the report. Importantly, several new latent topics emerged that were not originally identified. The new topics provide additional insights into the patient safety event data that would not otherwise easily be detected.Conclusion: The topic modeling approach provides a method to identify topics or themes that may not be immediately apparent and has the potential to allow for automatic reclassification of events that are ambiguously classified by the event reporter.