The research objective of this award is to develop broadly applicable methods to improve monitoring and management of patients with chronic diseases. The systems engineering based modeling methodology will generate effective predictions of disease progression over time. Further, they will be integrated with real-time feedback-driven forecasting and control/optimization algorithms to help clinicians determine the interval of time until a particular chronic disease patient should be monitored next or an intervention should be considered by a physician. The research links population-based knowledge to patient-specific information measurements taken sequentially to determine the optimum monitoring intervals. Preliminary research indicates that linear Gaussian system models are effective for modeling progression, but existing theory will be extended to include controlled observations that optimize the tradeoff between intervals that are too short or too long. By using data to generate partially observable state space models, and by using higher dimensional state spaces, we bring a new perspective to this problem.
The primary focus of this study is open-angle glaucoma, a major cause of blindness worldwide. If successful, the results of this research will indicate to clinicians when glaucoma progression has occurred and how chronic diseases such as glaucoma are likely to progress. In addition, our results will provide a recommendation on when to next monitor the patient. Such knowledge will improve the health outcomes of the population and also result in cost containment. The co-PIs will integrate these new methods into their courses and mentoring to impact graduate and undergraduate engineering students and medical students. Outcomes will be disseminated to engineering, public health, and medical communities through presentations and publications targeting highly visible journals in engineering and healthcare.
