Heart failure (HF) is a grave problem in the clinical and public health sectors. The aim of this study is to develop a phenotyping algorithm to identify patients with HF by using the medical information database network (MID-NET) in Japan. METHODS: From April 1 to December 31, 2013, clinical data of patients with HF were obtained from MID-NET. A phenotyping algorithm was developed with machine learning by using disease names, examinations, and medications. Two doctors validated the cases by manually reviewing the medical records according to the Japanese HF guidelines. The algorithm was also validated with different cohorts from an inpatient database of the Department of Cardiovascular Medicine at Tohoku University Hospital. RESULTS: The algorithm, which initially had low precision, was improved by incorporating the value of B-type natriuretic peptide and the combination of medications related to HF. Finally, the algorithm on a different cohort was verified with higher precision (35.0% → 87.8%). CONCLUSIONS: Proper algorithms can be used to identify patients with HF.
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