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

TL;DR

This study uses machine learning, specifically topic modeling, to analyze clinical notes of heart failure patients, uncovering key themes and comorbidities to improve understanding of patient conditions.

Contribution

It applies topic modeling to clinical notes of heart failure patients to identify major themes and comorbidities, providing insights into patient documentation.

Findings

Identified five major themes in clinical notes

Uncovered a theme related to heart disease comorbidities

Demonstrated the utility of topic modeling in clinical text analysis

Abstract

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities.