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

Journal: Computer methods and programs in biomedicine
PMID: 34959156

Abstract

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.

Authors

  • Takanori Yamashita
    Medical Information Center, Kyushu University Hospital, Fukuoka Japan. Electronic address: t-yama@med.kyushu-u.ac.jp.
  • Yoshifumi Wakata
    Kyushu University Hospital, Fukuoka, Japan.
  • Hideki Nakaguma
    Saiseikai Kumamoto Hospital, Kumamoto Japan.
  • Yasunobu Nohara
    Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.
  • Shinji Hato
    National Hospital Organization, Shikoku Cancer Center, Ehime Japan.
  • Susumu Kawamura
    National Hospital Organization, Shikoku Cancer Center, Ehime Japan.
  • Shuko Muraoka
    NTT Medical Center Tokyo, Tokyo Japan.
  • Masatoshi Sugita
    NTT Medical Center Tokyo, Tokyo Japan.
  • Mihoko Okada
    Institute of Health Data Infrastructure for all, Tokyo Japan.
  • Naoki Nakashima
    Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.
  • Hidehisa Soejima
    Saiseikai Kumamoto Hospital, Kumamoto Japan.

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