Applications of stochastic modeling and data analytics techniques in healthcare decision making
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We present approaches utilizing aspects of data analytics and stochastic modeling techniques and applied to various areas in healthcare. In general, the thesis has composed of three major components. Firtsly, we propose a comparison analysis between two of the very well-known infectious disease modeling techniques to derive effective vaccine allocation strategies. This study, has emerged from the fact that individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about the possible differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models. Secondly, we introduce a novel and holistic scheme to capture the gradual amyotrophic lateral sclerosis progression based on the critical events referred as tollgates. Amyotrophic lateral sclerosis is neuro-degenerative and terminal disease. Patients with amyotrophic lateral sclerosis lose control of voluntary movements over time due to continuous degeneration of motor neurons. Using a comprehensive longitudinal dataset from Mayo Clinic’s ALS Clinic in Rochester, MN, we characterize the progression through tollgates at the body segment (e.g., arm, leg, speech, swallowing, breathing) and patient levels over time. We describe how the progression based on the followed tollgate pathways varies among patients and ultimately, how this type of progression characterization may be utilized for further studies. Kaplan-Meier analysis are conducted to derive the probability of passing each tollgate over time. We observe that, in each body segment, the majority of the patients have their abilities affected or worse (Level1) at the first visit. Especially, the proportion of patients at higher tollgate levels is larger for arm and leg segments compared to others. For each segment, we derive the over-time progression pathways of patients in terms of the reached tollgates. Tollgates towards later visits show a great diversity among patients who were at the same tollgate level at the first clinic visit. The proposed tollgate mechanism well captures the variability among patients and the history plays a role on when patients reach tollgates. We suggest that further and comprehensive studies should be conducted to observe the whole effect of the history in the future progression. Thirdly, based on the fact that many available databases may not have detailed medical records to derive the necessary data, we propose a classification-based approach to estimate the tollgate data using ALSFRS-R scores which are available in most databases. We observed that tollgates are significantly associated with the ALSFRS-R scores. Multiclass classification techniques are commonly used in such problem; however, traditional classification techniques are not applicable to the problem of finding the tollgates due to the constraint of that a patients’ tollgates under a specific segment for multiple visit should be non-decreasing over time. Therefore, we propose two approaches to achieve a multi-class estimation in a non-decreasing manner given a classification method. While the first approach fixes the class estimates of observation in a sequential manner, the second approach utilizes a mixed integer programming model to estimate all the classes of a patients’ observations. We used five different multi-class classification techniques to be employed by both of the above implementations. Thus, we investigate the performance of classification model employed under both approaches for each body segment.
Cite this work
Ozden Onur Dalgic (2017). Applications of stochastic modeling and data analytics techniques in healthcare decision making. UWSpace. http://hdl.handle.net/10012/12754