The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database

Rieko Izukura, Tadashi Kandabashi, Yoshifumi Wakata, Chinatsu Nojiri, Yasunobu Nohara, Takanori Yamashita, Atsushi Takada, Jinsang Park, Yoshiaki Uyama, Naoki Nakashima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We aimed to develop rhabdomyolysis (RB) phenotyping algorithms using machine learning techniques and to create subphenotyping algorithms to identify RB patients who lack RB diagnosis. Two pattern algorithms, one with a focus on improving predictive value and one focused on improving sensitivity, were finally created and had a high area under the curve value of 0.846. Although we were unable to create subphenotyping algorithms, an attempt to detect unknown RB patients is important for epidemiological studies.

Original languageEnglish
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages1498-1499
Number of pages2
ISBN (Electronic)9781643680026
DOIs
Publication statusPublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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