| dc.description.abstract | 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. | en |
