Automatic knowledge extraction and data mining from echo reports of pediatric heart disease: Application on clinical decision support

Abstract

Echocardiography (Echo) reports of the patients with pediatric heart disease contain many disease related information, which provide great support to physicians for clinical decision. Such as treatment customization based on the risk level of the specific patient. With the help of natural language processing (NLP), information can be automatically extracted from free-text reports. Those structured data is much easier to analyze with the existing data mining approaches. In this study, we extract the entity/anatomic site-feature-value (EFV) triples in the Echo reports and predict the risk level on this basis. The prediction accuracy of machine learning and rule-based method are compared based on a manual prepared ideal data, to explore the application of automatic knowledge extraction on clinical decision support.