Secondary Usage of Electronic Health Record Data for Patient-Specific Modeling
Kim, Graham Karam
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Translational research has become an important bridge that moves findings from basic science research to patients' bedside and to the clinical community. Unfortunately, this notion of translational research seems to be unidirectional in that basic research is translated into clinical research and practice, but basic science research does not seem to benefit as much from clinical medicine. In my dissertation, I leverage the availability of retrospective EHR data and use them with biosimulation models to translate data from clinical medicine to benefit biosimulation modeling. Biosimulation models are mathematical representations of biological systems, and they can help with mechanistic understanding of physiology and predict the dynamics of a biological system. Using clinical data with biosimulation models has the potential to benefit both the biosimulation modelers, as well as clinicians. The abundance of retrospective clinical data available for research is a promising alternative to the traditional method of validating models by conducting resource-intensive prospective studies. These models can then be made patient-specific to simulate the physiology of individuals. When used in the clinical setting, these patient-specific models have the potential to be used by clinicians to better understand the underlying pathophysiology of the patient. In my research, I first conduct a scoping review of models in the literature to quantify model reproducibility and discover an appalling lack of model source code availability in publications. Then using a published hemodynamics model, I demonstrate using retrospective clinical dataset from right heart catheterizations to optimize and validate the model without needing to conduct burdensome prospective studies and explore potential clinical applications of patient-specific modeling. Finally, I describe an ontological approach to extend the data-model connection to be systematic and scalable. I demonstrate this approach by connecting cardiology data and lab results data with a hemodynamics model and several nephrology models, respectively.