Electronic Phenotyping to Identify Patients with Arrythmia Disease from a Hospital Information System

Abstract

Electronic phenotyping is an important method to identify a disease group by collecting clinical data from hospital information systems. This study aimed to extract accurate cases of supraventricular arrythmia, ventricular arrythmia, and bradycardia from clinical data of a hospital information system. The electronic phenotyping algorithm was improved using the machine learning method. Subsequently, it showed a higher area under the curve for prediction and higher specificity. However, the algorithm needs further improvement to classify each arrythmia disease accurately. In conclusion, phenotyping using clinical data from hospital information systems has some affinities and issues depending on the disease.