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

doi:10.3233/SHTI190503

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 proceeding › Conference contribution

2 Citations (Scopus)

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 language	English
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	1498-1499
Number of pages	2
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190503
Publication status	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	8/25/19 → 8/30/19

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI190503

Cite this

Izukura, R., Kandabashi, T., Wakata, Y., Nojiri, C., Nohara, Y., Yamashita, T., Takada, A., Park, J., Uyama, Y., & Nakashima, N. (2019). The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 1498-1499). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190503

The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database. / Izukura, Rieko; Kandabashi, Tadashi; Wakata, Yoshifumi et al.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 1498-1499 (Studies in Health Technology and Informatics; Vol. 264).

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

Izukura, R, Kandabashi, T, Wakata, Y, Nojiri, C, Nohara, Y, Yamashita, T, Takada, A, Park, J, Uyama, Y & Nakashima, N 2019, The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 1498-1499, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190503

Izukura R, Kandabashi T, Wakata Y, Nojiri C, Nohara Y, Yamashita T et al. The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1498-1499. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190503

Izukura, Rieko ; Kandabashi, Tadashi ; Wakata, Yoshifumi et al. / The development of an electronic phenotyping algorithm for identifying rhabdomyolysis patients in the Mid-NEt database. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 1498-1499 (Studies in Health Technology and Informatics).

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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.",

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

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

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