Machine learning for classification of postoperative patient status using standardized medical data

作者：Takanori Yamashita, Y. Wakata, Hideki Nakaguma, Yasunobu Nohara, Shinji Hato, Susumu Kawamura, Shuko Muraoka, Masatoshi Sugita, M. Okada, Naoki Nakashima, H. Soejima · 发表于：Comput. Methods Programs Biomed. · 年份：2021 · DOI：10.1016/j.cmpb.2021.106583 · 被引用次数：11 · 研究领域：Computer Science、Medicine

BACKGROUND AND OBJECTIVE Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analyze medical real-world data collected from different medical institutions. An electronic message and repository have been developed to link electronic medical records in this research project, which has simplified the data integration. Therefore, this paper proposes an analysis method and learning health systems to determine the priority of clinical intervention by clustering and visualizing time-series and prioritizing patient outcomes and status during hospitalization. METHODS Common data items for reimbursement (Diagnosis Procedure Combination [DPC]) and clinical pathway data were examined in this project at each participating institution that runs the verification test. Long-term hospitalization data were analyzed using the data stored in the cloud platform of the institutions' repositories using multiple machine learning methods for classification, visualization, and interpretation. RESULTS The ePath platform contributed to integrate the standardized data from multiple institutions. The distribution of DPC items or variances could be confirmed by clustering, temporal tendency through the directed graph, and extracting variables that contributed to the prediction and evaluation of SHapley Additive Explanation effects. Consti...