Date of Award
2020
Document Type
Open Access Master's Report
Degree Name
Master of Science in Health Informatics (MS)
Administrative Home Department
Department of Applied Computing
Advisor 1
Weihua Zhou
Committee Member 1
Guy Hembroff
Committee Member 2
Donald Peck
Abstract
The heterogeneous nature of today’s evolving health databases requires new techniques and approaches to process these data and extract clinically useful information. This relevant information obtained can be used to improve the response rate of cardiac resynchronization therapy (CRT) in patients with heart failure. Hierarchical clustering (HC) which is an unsupervised ML technique may uncover clusters within the bulk of data of patient population which is useful for strategies towards precision and personalized medicine. This study aims to investigate how HC can be used to automatically group a bulk of clinically acquired CRT data into clusters and subgroups that could confer clinically relevant information. About 165 patient data were used in the study and the analysis resulted in 4 different phenogroups with varying response rates. Some features were statistically significant when compared within the subgroups. Lastly, the study concludes that HC can be used to integrate and analyze different kinds of clinical data to aid in the identification of HF patient subgroups that are likely to respond to CRT.
Recommended Citation
Adeosun, Rukayat Bukola, "HIERARCHICAL CLUSTERING TO PREDICT THE RESPONSE OF CARDIAC RESYNCHRONIZATION THERAPY IN PATIENTS WITH HEART FAILURE", Open Access Master's Report, Michigan Technological University, 2020.