2022_Agarwal.pdf (256.49 kB)
Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure
journal contributionposted on 2023-02-22, 21:29 authored by A Agarwal, K Thirunarayan, WL Romine, A Alambo, Maan Isabella CajitaMaan Isabella Cajita, T Banerjee
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.
CitationAgarwal, A., Thirunarayan, K., Romine, W. L., Alambo, A., Cajita, M.Banerjee, T. (2022). Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2022-July, 2643-2646. https://doi.org/10.1109/EMBC48229.2022.9871400
PublisherInstitute of Electrical and Electronics Engineers (IEEE)