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


Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
16 Jun 2020
Historique:
entrez: 24 6 2020
pubmed: 24 6 2020
medline: 21 8 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32570602
pii: SHTI200385
doi: 10.3233/SHTI200385
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1247-1248

Auteurs

Kazuya Okamoto (K)

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan.

Takashi Yamamoto (T)

Patient Safety Unit, Kyoto University Hospital, Japan.

Shusuke Hiragi (S)

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan.

Shosuke Ohtera (S)

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan.

Osamu Sugiyama (O)

Preemptive Medicine & Lifestyle-Related Disease Research Center, Kyoto University Hospital, Japan.

Goshiro Yamamoto (G)

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan.

Masahiro Hirose (M)

Faculty of Medicine, Shimane University, Japan.

Tomohiro Kuroda (T)

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan.

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Classifications MeSH