• Login
    View Item 
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Doctor AI: Interpretable deep learning for modeling electronic health records

    Thumbnail
    View/Open
    CHOI-DISSERTATION-2018.pdf (5.031Mb)
    Date
    2018-05-23
    Author
    Choi, Edward
    Metadata
    Show full item record
    Abstract
    Deep learning recently has been showing superior performance in complex domains such as computer vision, audio processing and natural language processing compared to traditional statistical methods. Naturally, deep learning techniques, combined with large electronic health records (EHR) data generated from healthcare organizations have potential to bring dramatic changes to the healthcare industry. However, typical deep learning models can be seen as highly expressive blackboxes, making them difficult to be adopted in real-world healthcare applications due to lack of interpretability. In order for deep learning methods to be readily adopted by real-world clinical practices, they must be interpretable without sacrificing their prediction accuracy. In this thesis, we propose interpretable and accurate deep learning methods for modeling EHR, specifically focusing on longitudinal EHR data. We will be- gin with a direct application of a well-known deep learning algorithm, recurrent neural networks (RNN), to capture the temporal nature of longitudinal EHR. Then, based on the initial approach we develop interpretable deep learning models by focusing on three aspects of computational healthcare: efficient representation learning of medical concepts, code-level interpretation for sequence predictions, and leveraging domain knowledge into the model. Another important aspect that we will address in this thesis is developing a framework for effectively utilizing multiple data sources (e.g. diagnoses, medications, procedures), which can be extended in the future to incorporate wider data modalities such as lab values and clinical notes.
    URI
    http://hdl.handle.net/1853/60226
    Collections
    • College of Computing Theses and Dissertations [1191]
    • Georgia Tech Theses and Dissertations [23877]
    • School of Computational Science and Engineering Theses and Dissertations [100]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology