| dc.description.abstract | Advancements in data gathering and uncertainty surrounding health care and human
behavior require advanced modelling techniques such as Bayesian belief networks (BBNs) and
Markov networks (MNs) to analyze such a complex environment, and provide insights for decision
makers. The main objective of this dissertation is to develop probabilistic graphical models
(PGMs) to solve prediction and survival problems in health care domain. In this research, the aim
is to expand the knowledge for solving problems in this area by proposing various approaches
using BBNs, MNs and data mining models. In the first study, elastic net (EN) and BBN based noshow
prediction model for a primary care clinic is proposed. This study predicts the "no-show
probability of the patient(s)" using demographics, socio-economic status, and current appointment
information, and appointment attendance history of the patient and the family. The findings of noshow
prediction model is integrated into patient scheduling and overbooking policy. Using the
prediction model, we analyze the impact of integrating no-show prediction model into scheduling
for effective overbooking using discrete event simulation. Third, we propose a holistic Markovian
framework that incorporates different aspects of continuity, such as density, dispersion and
sequence and propose a framework for assessment of clinic/ provider CoC. Last study proposes a
multi-level survival analysis to define risk levels as low, medium, and high for kidney transplants
patients using deceased donor database. Our findings have showed that big data and uncertainty
challenge can be addressed by designing comprehensive frameworks combining expert knowledge
with data analytic models. | |
