Graph Clustering System for Text-Based Records in a Clinical Pathway
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 patients 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 patients 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.
Critical Pathways Data Mining Mathematical Computing
Takanori Yamashita Naoya Onimura Hidehisa Soejima Naoki Nakashima Sachio Hirokawa
Medical Information Center,Kyushu University Hospital,Fukuoka,Japan;Graduate School of Information S Graduate School of Information Science and Electrical Engineering,Kyushu University,Fukuoka,Japan Saiseikai Kumamoto Hospital,Kumamoto,Japan Medical Information Center,Kyushu University Hospital,Fukuoka,Japan Research Institute for Information Technology Kyushu University,Fukuoka,Japan
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
649-652
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）