Applying machine learning methods to electronic health records: studies in risk-stratification of chronic kidney disease and hypertrophic cardiomyopathy
| Author(s) | Bhattacharya, Moumita | |
| Date Accessioned | 2019-06-10T11:19:03Z | |
| Date Available | 2019-06-10T11:19:03Z | |
| Publication Date | 2019 | |
| SWORD Update | 2019-03-19T16:05:39Z | |
| Abstract | Electronic Health Records (EHRs) provide valuable clinical information that can be used toward disease prediction and patient risk-stratification. Applying machine learning methods to EHR data can enable identification of characteristic patterns in individuals and in whole populations. However, learning accurate models based on EHRs pose a number of challenges, including class imbalance and missing observations. In this thesis, we develop machine learning approaches for risk-stratification while effectively addressing challenges stemming from EHR analysis. We present our work in the context of chronic kidney disease (CKD) and hypertrophic cardiomyopathy (HCM), two common chronic conditions. ☐ We propose two approaches for addressing class imbalance. The first is a sampling-based ensemble method that attains high performance when used for stratifying CKD by severity levels. The second is an approach combining under- and over-sampling and an ensemble classifier that effectively identifies HCM patients at risk for adverse outcomes. To identify groups of co-occurring medical conditions among CKD patients, we introduce a probabilistic framework employing topic modeling in a non-traditional way. The obtained topics are clinically-meaningful, tight and distinct. Last, we present a framework utilizing supervised learning that effectively stratifies CKD patients by hospitalization risk. The models proposed in this thesis have much potential to assist healthcare providers in making clinical decisions. | en_US |
| Advisor | Shatkay, Hagit | |
| Degree | Ph.D. | |
| Department | University of Delaware, Department of Computer and Information Sciences | |
| DOI | https://doi.org/10.58088/80mf-0p88 | |
| Unique Identifier | 1104134682 | |
| URL | http://udspace.udel.edu/handle/19716/24242 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://search.proquest.com/docview/2201353912?accountid=10457 | |
| Title | Applying machine learning methods to electronic health records: studies in risk-stratification of chronic kidney disease and hypertrophic cardiomyopathy | en_US |
| Type | Thesis | en_US |