Month/Year of Graduation
12-2022
Degree Name
Bachelor of Science (B.S.)
Department
Interdisciplinary Informatics
First Advisor
Dario Ghersi
Abstract
The prediction of patient mortality in the healthcare system provides a metric by which hospitals can better manage patient care and assess the needs of each individual patient. As such, the development of better predictive methods is vital for improving patient outcomes and overall quality of care. Heart rate variability (HRV) is a measure of the heart’s complex beating patterns, giving medical professionals additional insight into patient health. Previous research has demonstrated the potential use of heart rate variability as a metric for patient mortality prediction for various conditions, however more work is necessary to validate HRV as a metric for a broader and more diverse set of patients. This study uses data from 2664 patients within the MIMIC-III clinical database matched with patient electrocardiogram (ECG) data to link HRV data with later patient mortality, examining the efficacy of HRV as a biomarker for predicting patient mortality and investigating possible avenues for future integration of HRV into patient mortality predictive algorithms.
Recommended Citation
Thiele, Matthew and Ghersi, Dario, "A Machine Learning Approach for Predicting Patient Mortality with Heart Rate Variability Statistics" (2022). Theses/Capstones/Creative Projects. 195.
https://digitalcommons.unomaha.edu/university_honors_program/195