Data_Sheet_1_Text Analysis of Electronic Medical Records to Predict Seclusion in Psychiatric Wards: Proof of Concept.docx

Aim: With the introduction of “Electronic Medical Record” (EMR) a wealth of digital data has become available. This provides a unique opportunity for exploring precedents for seclusion. This study explored the feasibility of text mining analysis in the EMR to eventually help reduce the use of seclusion in psychiatry.
Methods: The texts in notes and reports of the EMR during 5 years on an acute and non-acute psychiatric ward were analyzed using a text mining application. A period of 14 days was selected before seclusion or for non-secluded patients, before discharge. The resulting concepts were analyzed using chi-square tests to assess which concepts had a significant higher or lower frequency than expected in the “seclusion” and “non-seclusion” categories.
Results: Text mining led to an overview of 1,500 meaningful concepts. In the 14 day period prior to the event, 115 of these concepts had a significantly higher frequency in the seclusion category and 49 in the non-seclusion category. Analysis of the concepts from days 14 to 7 resulted in 54 concepts with a significantly higher frequency in the seclusion-category and 14 in the non-seclusion category.
Conclusions: The resulting significant concepts are comparable to reasons for seclusion in literature. These results are “proof of concept”. Analyzing text of reports in the EMR seems therefore promising as contribution to tools available for the prediction of seclusion. The next step is to build, train and test a model, before text mining can be part of an evidence-based clinical decision making tool.

研究目的：随着电子病历（Electronic Medical Record，EMR）系统的推广应用，海量数字化医疗数据应运而生，这为探究精神科隔离约束的应用先例提供了独特契机。本研究旨在探讨利用文本挖掘技术分析电子病历数据的可行性，以期最终助力减少精神科隔离约束的临床使用。
研究方法：本研究对某急性与非急性精神科病房5年间的病历笔记及报告文本展开分析，采用文本挖掘工具进行处理。研究设置两类对照时间窗口：其一为隔离约束事件发生前14天的患者数据，其二为非隔离患者出院前14天的对应数据。基于提取得到的概念特征，采用卡方检验（chi-square tests）分析其在‘隔离约束组’与‘非隔离约束组’中的频率差异，筛选出频率显著高于或低于预期的特征概念。
研究结果：通过文本挖掘共提取得到1500个具有临床意义的概念特征。在事件发生前14天的数据集内，隔离约束组中有115个概念特征的出现频率显著偏高，非隔离约束组则有49个。若将时间窗口限定为事件发生前14至7天，则隔离约束组中存在54个频率显著偏高的概念特征，非隔离约束组为14个。
研究结论：本研究提取得到的显著性特征概念与现有文献中记载的精神科隔离约束诱因具有较好的一致性，本研究结果属于‘概念验证’范畴。由此可见，对电子病历中的报告文本开展文本挖掘分析，有望为开发精神科隔离约束预测工具提供有力支撑。后续研究将构建、训练并测试相关模型，以期将文本挖掘技术纳入循证临床决策辅助工具体系。