Collaborative Research: Physical-Statistical Modeling and Optimization of Cardiovascular System

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The objective of this collaborative research project is to develop physical-statistical models of cardiovascular systems for optimizing medical decision making in spatiotemporal disease processes. Multi-scale computer models will be developed to improve the understanding of disease-altered cardiac electrical dynamics. In particular, the modeling will be done in multiple physical levels starting with ion channels, then cells, then tissues, and finally an anatomically realistic heart. Physics-based models will be statistically calibrated and adjusted so as to make more realistic predictions. Furthermore, an easy-to-evaluate statistical surrogate model will be developed for faster approximation, prediction and optimization, thereby facilitating real-time medical decision making. Physical-statistical models will be used in conjunction with sensor-based data fusion to optimize cardiovascular diagnostics. The simulation-based optimization approach provides a unique opportunity to search the optimal medical decisions with the "virtual" heart, as opposed to traditional "experience-based", "trial-and-error" or subjective decisions in the real-world heart. If successful, the results of this research will yield a fundamental understanding of the progression of cardiac diseases that is so vitally needed to improve preventive healthcare services. This research has the potential to make a paradigm shift in healthcare, i.e., from reactive care to preventive and proactive care, from experience-based to evidence-based cardiac care services. The early identification of cardiovascular diseases will decrease mortality rates, promote the timely delivery of life-saving interventions, and reduce healthcare cost (e.g., preventive care in lieu of expensive surgical interventions). This will positively impact cardiovascular patients, the largest population at risk of death in the US and in the world. This project will provide students with a unique opportunity to obtain multidisciplinary training in industrial and systems engineering, healthcare, statistics and optimization.