Operations Research & Statistical Learning Methods to Monitor the Progression of Glaucoma and Chronic Diseases

This thesis focuses on advancing operations research and statistical learning methods for medical decision making to improve the care of patients diagnosed with chronic conditions. Because the National Center for Disease Prevention (2020) estimates chronic conditions affect approximately 60% of the US adult population, improving the care of patients with chronic conditions will improve the lives of most Americans. Patients diagnosed with chronic conditions face lifestyle changes, rising treatment costs, and frequently reductions in quality of life. To improve the way in which clinicians treat patients with chronic conditions, treatment decisions can be supplemented by evidenced-based, data driven algorithmic decision-making methods. This thesis provides data-driven methodologies of a general nature that are instantiated for several medical decision-making problems. In chapter two we proactively identify the time of a patient’s primary open angle glaucoma (POAG) progression under high measurement error conditions using a soft voting ensemble classification model. When medical tests have low residual variability (e.g., empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. We present a solution to the latter case. We find rapid progression disease phases can be proactively identified with the combination of denoising and supervised classification methods. In chapter three, we determine the optimal time to next follow-up appointment for patients with the chronic condition of ocular hypertension (OHTN). Patients with OHTN are at increased risk of developing glaucoma and should be observed over their lifetime. Follow-up appointment schedules that are chosen poorly can result in, at minimum, delay in the detection of a patient’s progression to glaucoma, and at worse, yield poor patient outcomes. To this end, we present a personalized decision support algorithm that uses the fitted Q-iteration reinforcement learning algorithm to recommend personalized time-to-next follow-up schedules that are based on a patient’s medical state. We find personalized follow-up appointments schedules produced by reinforcement learning methods are superior to both 1-year and 2-year fixed interval follow-up appointment schedules. In chapters four and five, we examine and compare several criteria for determining progression from OHTN to POAG and evaluate the use of a collective POAG conversion rule in predicting future occurrences of patients' POAG conversion. We find age, race, and sex are statistically significant determinants in progression for all compared criteria. However, there exists broad conversion discordance between the criteria, as demonstrated by statistically different survival curves and the limited overlap in eyes that progressed by multiple criteria. Ultimately, to permit machine learning models to predict conversion from OHTN to POAG, it is essential to have quantitative reference standards for POAG conversion for researchers to use. Additionally, using the collective POAG conversion rule, we find machine learning models can successfully predict future OHTN conversion events to POAG. This research was conducted in collaboration with clinical disease/domain experts. All the medical decision-making research herein addresses real world healthcare issues, that, if solved, have the potential to improve vision care if implemented. While these methodologies primarily focus on chronic conditions affecting the eyes (e.g., OHTN and POAG), it is important to note that much of the work produced offers methods applicable to other chronic diseases.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169926/1/isaacaj_1.pd