Yamashita Takanori, Onimura Naoya, Soejima Hidehisa, Nakashima Naoki, Hirokawa Sachio
Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
Stud Health Technol Inform. 2017;245:649-652.
The progressive digitization of medical records has resulted in the accumulation of large amounts of data. Electronic medical data include structured numerical data and unstructured text data. Although text-based medical record processing has been researched, few studies contribute to medical practice. The analysis of unstructured text data can improve medical processes. Hence, this study presents a clustering approach for detecting typical patient's condition from text-based medical record of clinical pathway. In this approach, the sentences in a cluster are merged to generate a "sentence graph" of the cluster after classified feature word by Louvain method. An analysis of real text-based medical records indicates that sentence graphs can represent the medical treatment and patient's condition in a medical process. This method could help the standardization of text-based medical records and the recognition of feature medical processes for improving medical treatment.
医疗记录的逐步数字化导致了大量数据的积累。电子医疗数据包括结构化数值数据和非结构化文本数据。尽管基于文本的医疗记录处理已得到研究，但很少有研究对医疗实践有帮助。对非结构化文本数据的分析可以改善医疗流程。因此，本研究提出一种聚类方法，用于从临床路径的基于文本的医疗记录中检测典型患者病情。在这种方法中，通过Louvain方法对特征词进行分类后，将一个聚类中的句子合并以生成该聚类的“句子图”。对真实的基于文本的医疗记录的分析表明，句子图可以在医疗过程中表示医疗治疗和患者病情。该方法有助于基于文本的医疗记录的标准化以及特征医疗流程的识别，以改善医疗治疗。
