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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences
Yuanbo (Cody) Wang
Defends his thesis:
Building a Systematic Analytic Pipeline – Big Data Innovation in Healthcare
Monday, August 5th, 2019
1:00 PM Eastern Time
BME/Whitaker Building, Room 1103
Thesis Advisor:
Dr. Eva Lee
School of Industrial and Systems Engineering
Georgia Institute of Technology
Committee Members:
Dr. King Jordan
School of Biological Sciences
Georgia Institute of Technology
Dr. Fredrik Vannberg
School of Biological Sciences
Georgia Institute of Technology
Dr. Yajun Mei
School of Industrial and Systems Engineering
Georgia Institute of Technology
Dr. Alfred Merrill
School of Biological Sciences
Georgia Institute of Technology
Dr. Shatavia Morrison
Division of Bacterial Diseases
Centers for Disease Control and Prevention
Abstract
Data-driven healthcare utilizing big data in Electronic Health Records (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 EHR 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, each of which plays an 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. My 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.
