NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation

Abstract

Chronic Obstructive Pulmonary Disease (COPD) frequently coincides with other comorbidities such as congestive heart failure, hypertension, coronary artery disease, or atrial fibrillation. The exhibition of overlapping sets of symptoms associated with these conditions prevents early identification of an acute exacerbation upon admission to a hospital. Early identification of the underlying cause of exacerbation allows timely prescription of an optimal treatment plan as well as allows avoiding unnecessary clinical tests and specialist consultations. The aim of this study was to develop a predictive model for early identification of COPD exacerbation by using the clinical notes generated within 24 hours of admission to the hospital. The study cohort included patients with a prior diagnosis of COPD. Four predictive models have been developed, among which the support vector machine showed the best performance based on the resulting 80% F1 score.