Utilizing Narrative Text from Electronic Health Records for Early Warning Model of Chronic Disease

Chronic diseases are associated with high morbidity and mortality, and they cannot be quickly determined and identified during their early stages. Therefore, early identification and warning of chronic diseases are important. This study proposed an early warning model (EWM) based on electronic health records (EHRs). The model included comprehensive methods that identify whether the patient is expected to suffer from chronic disease and provide early warning based on the undiagnosed narrative text of EHRs. A professional medical terminology library called Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) was utilized to improve the accuracy of early warning. The model utilized semantic relationships in the SNOMED CT to expand the effects of related terms of renal cancer. We utilized 1,300 medical records and more than 1,400 progress notes in the EHRs in our experiments. The proposed EWM of renal cancer achieved a precision of 90%, recall of 91%, and F-measure of 91%.