Data-driven modeling for uncertain biological systems

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Authors
Howsmon, Daniel P.
Issue Date
2017-05
Type
Electronic thesis
Thesis
Language
ENG
Keywords
Chemical engineering
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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.
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May 2017
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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