Title:
Building a systematic analytic pipeline – big data innovation in healthcare
Building a systematic analytic pipeline – big data innovation in healthcare
Author(s)
Wang, Yuanbo
Advisor(s)
Jordan, I. King
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Abstract
Electronic Health Records (EHR) containing large amount of patient data present both opportunities and challenges to industry, policy makers, and researchers. Data-driven healthcare utilizing big data in EHR has the potential to revolutionize care delivery while reducing costs. However, for researchers, policymakers, and practitioners to take full advantage of the benefits that electronic records can provide, several challenges must be addressed: 1) Extraction and coding methods for EHR data must be strategically designed to address issues of data quantity, quality, and patient confidentiality; 2) Standardization of clinical terminologies is essential in facilitating interoperability among EHR systems and allows for multi-site comparative effectiveness studies; 3) Effective methods for mining longitudinal health data common in the EHR are critical for revealing disease progression, treatment patterns, and patient similarities, all of which play important role in clinical decision support and treatment improvement; 4) Advanced machine learning techniques are necessary for early detection and prognosis of disease and identifying critical factors that impact patient outcome and; 5) Practical intervention strategies must be developed to address healthcare disparity in rural and remote areas with lack of resources and access. This thesis focuses on these five issues by developing a systematic analytic pipeline for big data in healthcare. Specifically, innovative strategies are developed for information extraction, clinical terminology mapping, time-series mining and clustering, feature selection and discriminatory modeling. Finally, practical implementation methods for telehealth services are designed to reduce healthcare disparity in underserved rural Appalachian counties in Georgia.
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Date Issued
2019-08-27
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Text
Resource Subtype
Dissertation