Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them. To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of documents obtained from sparsified text vector similarities. We showcase the approach with the analysis of incident reports submitted in Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals levels of meaning with different resolution in the topics of the dataset, as shown by relevant descriptive terms extracted from the groups of records, as well as by comparing a posteriori against hand-coded categories assigned by healthcare personnel. Our content communities exhibit good correspondence with well-defined hand-coded categories, yet our results also provide further medical detail in certain areas as well as revealing complementary descriptors of incidents beyond the external classification. We also discuss how the method can be used to monitor reports over time and across different healthcare providers, and to detect emerging trends that fall outside of pre-existing categories.

中文翻译：

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从自由文本到健康记录中的内容簇：一种无监督的图形分区方法。

电子医疗记录包含大量不同形式的非结构化数据。自由文本构成了此类数据的很大一部分，但是由于缺乏及时提取可解释内容的适当方法，因此，如此丰富的详细信息源在实践中经常未被充分利用。在这里，我们将网络理论工具应用于英国国家卫生局的医院患者事件报告中的自由文本分析，以直接基于其中包含的自由文本描述的无监督方式和不同分辨率级别查找报告簇。为此，我们将最近开发的基于段落向量的深度神经网络文本嵌入方法与应用于从稀疏文本向量相似度获得的文档的相似度图的多尺度马尔可夫稳定性社区检测相结合。我们通过分析在伦敦帝国理工学院NHS信托基金中提交的事件报告来展示该方法。多尺度社区结构揭示了数据集主题中具有不同分辨率的含义级别，如从记录组中提取的相关描述性术语所示，以及通过将后验与医护人员分配​​的手工编码类别进行比较，表明了这一含义。我们的内容社区与定义明确的手工编码类别具有良好的对应关系，然而，我们的结果还提供了某些领域的进一步医学细节，并揭示了外部分类之外的事件补充描述。我们还将讨论如何使用该方法来监视一段时间内以及不同医疗保健提供者之间的报告，以及检测超出现有类别的新兴趋势。