Using NLP for Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation

Patients with Chronic Obstructive Pulmonary Disease (COPD) frequently have other comorbidities such as congestive heart failure, hypertension, coronary artery disease, or atrial fibrillation. These conditions exhibit an overlapping set of symptoms which complicates early identification of the primary cause of an acute exacerbation upon admission to a hospital. Timely identification of the underlying condition that led to an acute exacerbation allows expedite prescription of an optimal care plan and reduce unnecessary medical procedures. The aim of this study was to develop a classification model using data from electronic health records for early identification of COPD exacerbation in newly admitted patients. The study cohort included patients with a prior diagnosis of COPD. Three predictive models have been developed among which random forest showed the best performance and resulted in 73% F1score and accuracy of 80%.