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    Data-driven modeling for uncertain biological systems

    Author
    Howsmon, Daniel P.
    View/Open
    179098_Howsmon_rpi_0185E_11167.pdf (2.198Mb)
    Other Contributors
    Hahn, Juergen; Bequette, B. Wayne; Koffas, Mattheos A. G.; Julius, Anak Agung;
    Date Issued
    2017-05
    Subject
    Chemical engineering
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/2231
    Abstract
    Open-loop data was used for training and then the SF detection algorithm was applied in real-time during a closed-loop clinical trial, achieving 88.0% sensitivity and only issuing 0.22 false positives per day. This is the first report of real-time SF detection in an artificial pancreas and the retrospective results are superior to those reported in previous retrospective studies using clinical data. Project (2) analyzes potential urine- and blood-based biomarkers for autism spectrum disorder, reporting a biomarker based on folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) that achieves the highest classification accuracy to date. Project (3) uses neural networks to determine relationships between host cytokines and bacterial metabolites on the differentiation of T cells prior to adoptive transfer, uncovering novel interactions and suggesting optimal differentiation protocols. It is emphasized that the models developed in all projects are evaluated through validation data sets or cross-validation to assess the predictability of these models, rather than just their description of specific data sets.; Data-driven models couple any systems, statistical, or optimization-based model to a particular application area and model objective. These models can operate in the midst of large amounts of model uncertainty since they do not require fundamental knowledge of the system of interest. This is especially advantageous when the system is poorly understood since data-driven models are not constrained by possibly false hypotheses and relationships, allowing the unveiling of new relationships that would otherwise go unnoticed. This thesis explores data-driven modeling in three areas of interest. Project (1) develops an insulin infusion set failure (SF) detection algorithm using glucose measurements and insulin dosing schedules of patients with type 1 diabetes.;
    Description
    May 2017; School of Engineering
    Department
    Dept. of Chemical and Biological Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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