@inproceedings{7e504660baa544daabae39f8e89e6ec9,
title = "Detecting severe incidents from electronic medical records using machine learning methods",
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 = "Medical records, Safety management, Supervised machine learning",
author = "Kazuya Okamoto and Takashi Yamamoto and Shusuke Hiragi and Shosuke Ohtera and Osamu Sugiyama and Goshiro Yamamoto and Masahiro Hirose and Tomohiro Kuroda",
note = "Publisher Copyright: {\textcopyright} 2020 European Federation for Medical Informatics (EFMI) and IOS Press.; 30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",
year = "2020",
month = jun,
day = "16",
doi = "10.3233/SHTI200385",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1247--1248",
editor = "Pape-Haugaard, \{Louise B.\} and Christian Lovis and Madsen, \{Inge Cort\} and Patrick Weber and Nielsen, \{Per Hostrup\} and Philip Scott",
booktitle = "Digital Personalized Health and Medicine - Proceedings of MIE 2020",
address = "Netherlands",
}