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The Development of an Electronic Phenotyping Algorithm for Identifying Rhabdomyolysis Patients in the MID-NET

Rieko Izukura¹, Tadashi Kandabashi¹, Yoshifumi Wakata²

¹Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.

Studies in Health Technology and Informatics

|August 24, 2019

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.

Keywords:

Algorithms Machine learning Rhabdomyolysis

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