Automated Knowledge Discovery in Healthcare from Complex Data with Covariates
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PublisherThe University of Arizona.
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EmbargoRelease after 05/29/2022
AbstractIn this dissertation, the Phase-Type (PH) distribution is studied with consideration of covariates to model and investigate the patient flow information. The length-of-stay (LOS) data of patients with distinct diseases are analyzed in terms of the complete hospital stay or the visit in each department respectively. By PH distribution modeling, the patients clustering and the influences of each covariate are obtained. Comparisons and evaluations among different diseases, different LOS groups, distinct transfer routes and impacts of covariates are implemented. First, we apply the Coxian PH distributions for fitting the patient flow information collected in a hospital of patients of distinct diseases, including headache, liveborn infant, alcohol abuse, acute upper respiratory infection, and secondary cataract. Based on the results obtained by fitting Coxian PH distributions to the LOS data, the patients can be divided into different groups. The sharing common characteristics, readmission rates, and discharge destinations are evaluated and compared among different disease. Second, we use the Coxian PH distributions with covariates to fit the patient flow information of both geriatric patients and Alcohol use disorder (AUD) patients collected in a hospital. The influences of the covariates of age, gender, admission type, admit source, and financial class on LOS are assessed and compared through Expectation-Maximization (EM) algorithms. The estimated distributions can classify those patients into different LOS groups and the estimated coefficients and the statistical significance of covariate effects are also achieved and analyzed. Last, the Coxian PH distributions are integrated in an aggregated Markov chain to model the sequences of LOS in each department that geriatric patients visit during their hospital stay. The aggregated PH distribution is fitted to the intra-hospital transfer flow route by using Maximum Likelihood Estimation (MLE) to obtain the transition rates within each department and transfer rates among departments. The associations between shared characteristics and the transfer routes are also verified. We conclude that the analysis of associations between LOS groups and readmission rates can help avoid waste of sources. The top predictors are also studied with respect to practical reasons, which may provide guideline for decision-making and resource allocation in healthcare field. The intra-transfer routes within a hospital are associated with the characteristics of patients at admission and discharge.
Degree ProgramGraduate College
Systems & Industrial Engineering