Deep-learning-based Natural-language-processing Models To Identify Cardiovascular Disease Hospitalisations Of Patients With Diabetes From Routine Visits’ Text

This article discusses the use of natural language processing (NLP) to automatically identify hospitalizations related to cardiovascular disease (CVDs) from free-form text describing routine visits of patients with diabetes. Four possible time windows of increasing level of expected difficulty were considered: infinite, 24 months, 12 months, and 6 months. Results showed that the proposed NLP approach was successful for both the infinite and 24-month windows, while performance deteriorated with shorter time windows. Possible clinical applications of tools based on the proposed NLP approach include the retrospective filling of medical records with respect to a patient’s CVD history for epidemiological and research purposes as well as for clinical decision making.