報告題目：Pattern Discovery of Health Curves using an Ordered Probit Model with Bayesian Smoothing and Functional PCA
報 告 人：汪時嘉
報告地點：騰訊會議 (會議ID：808 500 651)
汪時嘉博士畢業于加拿大西蒙菲莎大學，現為南開大學統計與數據科學學院助理教授。曾在Systematic Biology, Neural Information Processing Systems, Bioinformatics、Canadian Journal of Statistics等期刊會議發表多篇SCI論文。
This article is motivated by the need of discovering patterns of patients' health based on their daily settings of care for the purpose of aiding the health policy-makers to improve the effectiveness of distributing funding for health services. The hidden process of one's health status is assumed to be a continuous smooth function, called the health curve, ranging from perfectly healthy to dead. The health curves are linked to the categorical setting of care using an ordered probit model and are inferred through Bayesian smoothing. The challenges include the nontrivial constraints on the lower bound of the health status (death) and on the model parameters to ensure model identifiability. We use the Markov chain Monte Carlo method to estimate the parameters and the health curves. The functional principal component analysis is applied to the patients' estimated health curves to discover common health patterns. The proposed method is demonstrated through an application to patients hospitalized from strokes in Ontario. Whilst this paper focuses on the method's application to a health care problem, the proposed model and its implementation have the potential to be applied to many application domains in which the response variable is ordinal and there is a hidden process.
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撰稿：劉柏森 審核：徐強 單位：統計學院