Introduction Epileptic and psychogenic non-epileptic seizures (PNES) are common diagnostic problems encountered in hospital practice. This study explores the use of unsupervised machine learning in discovering themes in medical records of patients presenting with PNES. We hypothesised that themes generated by machine learning are comparable with the classification by human experts. Methods This is a retrospective analysis of the medical records in the emergency department of patients (age >18 years) with PNES who underwent inpatient video-electroencephalography monitoring from May 2009 to June 2014 and received a final diagnosis of PNES. Prior to machine learning of written text, we applied a standardised approach in natural language processing to create a document-term matrix (removal of numbers, stop-words and punctuations, transforming fonts to lower case). The words were separated into tokens and treated as if existing within a bag-of-words. A probability of each word existing within a topic (theme) was modelled on multivariate Dirichlet distribution (R Foundation, V.3.5.0). Next, we asked four experts to independently provide a clinical interpretation of the generated topics. When the majority of (≥3) experts agreed, it was regarded as highly congruent. Interactive data are available on the web at (). Results There were 39 patients (74.4% women, median age 35 years with range 20–82). A total of 121 documents were converted to text files for text mining. There were 15 generated topics with 12/15 topics rated as highly congruent. The main themes were about descriptors of seizures and medication use. Conclusions The findings from machine learning on PNES-related documentation provides evidence for the feasibility of applying machine-learning methodology to analyse large volumes of medical records. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records.

中文翻译：

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在心因性非癫痫发作患者的病历中发现主题

简介 癫痫和心因性非癫痫发作 (PNES) 是医院实践中常见的诊断问题。本研究探讨了使用无监督机器学习来发现 PNES 患者病历中的主题。我们假设机器学习产生的主题与人类专家的分类相当。方法回顾性分析2009年5月至2014年6月接受住院视频脑电图监测并最终诊断为PNES的PNES患者（年龄>18岁）急诊科病历。在书面文本的机器学习之前，我们在自然语言处理中应用标准化方法来创建文档术语矩阵（去除数字、停用词和标点符号，将字体转换为小写）。这些词被分成标记并被视为存在于词袋中。主题（主题）中存在的每个单词的概率基于多元 Dirichlet 分布（R Foundation，V.3.5.0）建模。接下来，我们请四位专家独立提供生成主题的临床解释。当大多数（≥3）专家同意时，它被认为是高度一致的。交互式数据可在网络上（当大多数（≥3）专家同意时，它被认为是高度一致的。交互式数据可在网络上（当大多数（≥3）专家同意时，它被认为是高度一致的。交互式数据可在网络上（）。结果 共有 39 名患者（74.4% 为女性，中位年龄 35 岁，范围 20-82）。共有 121 个文档被转换为文本文件进行文本挖掘。有 15 个生成的主题，其中 12/15 的主题被评为高度一致。主题是关于癫痫发作和药物使用的描述。结论机器学习对 PNES 相关文档的发现为应用机器学习方法分析大量医疗记录的可行性提供了证据。机器学习产生的主题与临床医生的解释一致，表明该方法可用于在大量医疗记录中筛查医疗状况。