Data-driven modeling for uncertain biological systems
Authors
Howsmon, Daniel P.
ORCID
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Other Contributors
Hahn, Juergen
Bequette, B. Wayne
Koffas, Mattheos A. G.
Julius, Anak Agung
Bequette, B. Wayne
Koffas, Mattheos A. G.
Julius, Anak Agung
Issue Date
2017-05
Keywords
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.
Full Citation
Abstract
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
School of Engineering
Department
Dept. of Chemical and Biological Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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