报告题目：Pattern Discovery of Health Curves using an Ordered Probit Model with Bayesian Smoothing and Functional PCA
报 告 人：汪时嘉
报告地点：腾讯会议 (会议ID：808 500 651)
汪时嘉博士毕业于加拿大西蒙菲莎大学，现为南开大学统计与数据科学学院助理教授。曾在Systematic Biology, Neural Information Processing Systems, Bioinformatics、Canadian Journal of Statistics等期刊会议发表多篇SCI论文。
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：刘柏森 审核：徐强 单位：统计学院