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

Ankita Agarwal, Krishnaprasad Thirunarayan, William L. Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageAmerican English
Title of host publication2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherIEEE
Pages2643-2646
Number of pages4
ISBN (Electronic)978-1-7281-2782-8
ISBN (Print)978-1-7281-2783-5
DOIs
StatePublished - Sep 8 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
Duration: Jul 11 2022Jul 15 2022

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period7/11/227/15/22

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

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