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Heart failure occurs when the heart is not able to pump blood and oxygen tosupport other organs in the body as it should. Treatments include medicationsand sometimes hospitalization. Patients with heart failure can have bothcardiovascular as well as non-cardiovascular comorbidities. Clinical notes ofpatients with heart failure can be analyzed to gain insight into the topicsdiscussed in these notes and the major comorbidities in these patients. In thisregard, we apply machine learning techniques, such as topic modeling, toidentify the major themes found in the clinical notes specific to theprocedures performed on 1,200 patients admitted for heart failure at theUniversity of Illinois Hospital and Health Sciences System (UI Health). Topicmodeling revealed five hidden themes in these clinical notes, including onerelated to heart disease comorbidities.

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure