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DEEP NEURAL NETWORKS FOR COMPLEX DISEASE PREDICTION USING ELECTRONIC HEALTH RECORDS AND GENOMIC DATA

Javidi, Hamed
http://orcid.org/0000-0002-6332-1229
http://rave.ohiolink.edu/etdc/view?acc_num=csu1717679445956948

Abstract Details

2024, Doctor of Philosophy in Engineering, Cleveland State University, Washkewicz College of Engineering.
Leveraging electronic health record data requires sophisticated methods that can optimally process this information to improve clinical decision-making. Artificial Intelligence (AI) promises to process healthcare data faster, for lower costs, and more accurately than conventional processes. Deep learning applied to longitudinal electronic health records (EHR) holds promise for disease prediction, but a systematic methods comparison has yet to be reported. Despite the promises of this technology, challenges remain in the current approaches to predicting a disease. There remains an unmet need for developing a gold-standard disease prediction framework for EHR data that can be reliably applied across many diseases. This research proposes a generalized deep learning approach that is amenable to predicting a vast number of diseases by integrating multiple streams of longitudinal clinical data and genomic features to maximize the predictive power over a broad spectrum of diseases. I provide empirical validation of the proposed solution using data from multiple datasets; including comprehensive simulated datasets and a real-world EHR datasets. The ultimate goal of this research is to develop a generalized deep learning approach that is amenable to predicting a vast number of diseases using longitudinal clinical data from the EHR.
Daniel Rotroff (Advisor)
Donald Allensworth-Davies (Committee Member)
Hongkai Yu (Committee Member)
Sathish Kumar (Committee Member)
155 p.
Computer Science
Deep learning, Deep neural network, Pediatric records, Type 2 diabetes, Prediabetes, Metabolic disease, Longitudinal data, Electronic Health Record (EHR), Whole-genome prediction, Interpretable machine learning, Pediatric disease prediction

Recommended Citations

Citations

  • Javidi, H. (2024). DEEP NEURAL NETWORKS FOR COMPLEX DISEASE PREDICTION USING ELECTRONIC HEALTH RECORDS AND GENOMIC DATA [Doctoral dissertation, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1717679445956948

    APA Style (7th edition)

  • Javidi, Hamed. DEEP NEURAL NETWORKS FOR COMPLEX DISEASE PREDICTION USING ELECTRONIC HEALTH RECORDS AND GENOMIC DATA. 2024. Cleveland State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1717679445956948.

    MLA Style (8th edition)

  • Javidi, Hamed. "DEEP NEURAL NETWORKS FOR COMPLEX DISEASE PREDICTION USING ELECTRONIC HEALTH RECORDS AND GENOMIC DATA." Doctoral dissertation, Cleveland State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=csu1717679445956948

    Chicago Manual of Style (17th edition)

csu1717679445956948
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© 2024, all rights reserved.
This open access ETD is published by Cleveland State University and OhioLINK.