Chronic Obstructive Pulmonary Disease (COPD) exacerbation exhibits a set of overlapping symptoms with various forms of cardiovascular disease, which makes its early identification challenging. Timely identification of the underlying condition that caused acute admission of COPD patients in the emergency room (ER) may improve patient care and reduce care costs. This study aims to use machine learning combined with natural language processing (NLP) of ER notes to facilitate differential diagnosis in COPD patients admitted to ER. Using unstructured patient information extracted from the notes documented at the very first hours of admission to the hospital, four machine learning models were developed and tested. The random forest model demonstrated the best performance with F1 score of 93%.