Abstract
Times to multiple events (TMEs) are a major data type in large- scale business and medical data. Despite its im-portance, the analysis of TME data has not been well studied be cause of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Ba yesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the elec-tronic health records of 2,111 patients in a children’s hospita l in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in larg e-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analy-sis method.
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