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.
| Original language | American English |
|---|---|
| Title of host publication | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
| Publisher | IEEE |
| Pages | 2643-2646 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-1-7281-2782-8 |
| ISBN (Print) | 978-1-7281-2783-5 |
| DOIs | |
| State | Published - Sep 8 2022 |
| Event | 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom Duration: Jul 11 2022 → Jul 15 2022 |
Conference
| Conference | 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 |
|---|---|
| Country/Territory | United Kingdom |
| City | Glasgow |
| Period | 7/11/22 → 7/15/22 |
ASJC Scopus Subject Areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Keywords
- Heart
- Heart Diseases
- Heart Failure
- Hospitalization
- Hospitals
- Humans
- Comobidites
- Computer Science--Machine learning
- Text mining
- Topic modeling
Disciplines
- Computer Sciences
- Engineering