Literature DB >> 32570602

Detecting Severe Incidents from Electronic Medical Records Using Machine Learning Methods.

Kazuya Okamoto1, Takashi Yamamoto2, Shusuke Hiragi1, Shosuke Ohtera1, Osamu Sugiyama3, Goshiro Yamamoto1, Masahiro Hirose4, Tomohiro Kuroda1.   

Abstract

The goal of this research was to design a solution to detect non-reported incidents, especially severe incidents. To achieve this goal, we proposed a method to process electronic medical records and automatically extract clinical notes describing severe incidents. To evaluate the proposed method, we implemented a system and used the system. The system successfully detected a non-reported incident to the safety management department.

Keywords:  Safety management; medical records; supervised machine learning

Year:  2020        PMID: 32570602     DOI: 10.3233/SHTI200385

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.

Authors:  Xingyu Zhou; Xianying Li; Zijun Zhang; Qinrong Han; Huijiao Deng; Yi Jiang; Chunxiao Tang; Lin Yang
Journal:  Front Physiol       Date:  2022-09-29       Impact factor: 4.755

  1 in total

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