Detecting severe incidents from electronic medical records using machine learning methods

Kazuya Okamoto; Takashi Yamamoto; Shusuke Hiragi; Shosuke Ohtera; Osamu Sugiyama; Goshiro Yamamoto; Masahiro Hirose; Tomohiro Kuroda

doi:10.3233/SHTI200385

Detecting severe incidents from electronic medical records using machine learning methods

Kazuya Okamoto, Takashi Yamamoto, Shusuke Hiragi, Shosuke Ohtera, Osamu Sugiyama, Goshiro Yamamoto, Masahiro Hirose, Tomohiro Kuroda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Digital Personalized Health and Medicine - Proceedings of MIE 2020
Editors	Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
Publisher	IOS Press
Pages	1247-1248
Number of pages	2
ISBN (Electronic)	9781643680828
DOIs	https://doi.org/10.3233/SHTI200385
Publication status	Published - 16-06-2020
Externally published	Yes
Event	30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland Duration: 28-04-2020 → 01-05-2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	270
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	30th Medical Informatics Europe Conference, MIE 2020
Country/Territory	Switzerland
City	Geneva
Period	28-04-20 → 01-05-20

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI200385

Cite this

Okamoto, K., Yamamoto, T., Hiragi, S., Ohtera, S., Sugiyama, O., Yamamoto, G., Hirose, M., & Kuroda, T. (2020). Detecting severe incidents from electronic medical records using machine learning methods. In L. B. Pape-Haugaard, C. Lovis, I. C. Madsen, P. Weber, P. H. Nielsen, & P. Scott (Eds.), Digital Personalized Health and Medicine - Proceedings of MIE 2020 (pp. 1247-1248). (Studies in Health Technology and Informatics; Vol. 270). IOS Press. https://doi.org/10.3233/SHTI200385

Okamoto, Kazuya ; Yamamoto, Takashi ; Hiragi, Shusuke et al. / Detecting severe incidents from electronic medical records using machine learning methods. Digital Personalized Health and Medicine - Proceedings of MIE 2020. editor / Louise B. Pape-Haugaard ; Christian Lovis ; Inge Cort Madsen ; Patrick Weber ; Per Hostrup Nielsen ; Philip Scott. IOS Press, 2020. pp. 1247-1248 (Studies in Health Technology and Informatics).

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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",

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editor = "Pape-Haugaard, {Louise B.} and Christian Lovis and Madsen, {Inge Cort} and Patrick Weber and Nielsen, {Per Hostrup} and Philip Scott",

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Okamoto, K, Yamamoto, T, Hiragi, S, Ohtera, S, Sugiyama, O, Yamamoto, G, Hirose, M & Kuroda, T 2020, Detecting severe incidents from electronic medical records using machine learning methods. in LB Pape-Haugaard, C Lovis, IC Madsen, P Weber, PH Nielsen & P Scott (eds), Digital Personalized Health and Medicine - Proceedings of MIE 2020. Studies in Health Technology and Informatics, vol. 270, IOS Press, pp. 1247-1248, 30th Medical Informatics Europe Conference, MIE 2020, Geneva, Switzerland, 28-04-20. https://doi.org/10.3233/SHTI200385

Detecting severe incidents from electronic medical records using machine learning methods. / Okamoto, Kazuya; Yamamoto, Takashi; Hiragi, Shusuke et al.
Digital Personalized Health and Medicine - Proceedings of MIE 2020. ed. / Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. IOS Press, 2020. p. 1247-1248 (Studies in Health Technology and Informatics; Vol. 270).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detecting severe incidents from electronic medical records using machine learning methods

AU - Okamoto, Kazuya

AU - Yamamoto, Takashi

AU - Hiragi, Shusuke

AU - Ohtera, Shosuke

AU - Sugiyama, Osamu

AU - Yamamoto, Goshiro

AU - Hirose, Masahiro

AU - Kuroda, Tomohiro

PY - 2020/6/16

Y1 - 2020/6/16

N2 - 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.

AB - 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.

KW - Medical records

KW - Safety management

KW - Supervised machine learning

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ER -

Okamoto K, Yamamoto T, Hiragi S, Ohtera S, Sugiyama O, Yamamoto G et al. Detecting severe incidents from electronic medical records using machine learning methods. In Pape-Haugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH, Scott P, editors, Digital Personalized Health and Medicine - Proceedings of MIE 2020. IOS Press. 2020. p. 1247-1248. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI200385

Detecting severe incidents from electronic medical records using machine learning methods

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All Science Journal Classification (ASJC) codes

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