Online Risk Monitoring of Multiple Diseases by a Dynamic Screening System

Music City Center 

Disease early detection and prevention (DEDP) is an important topic in medical and public health research. Because disease risk factors are usually observed sequentially over time, DEDP is a sequential decision-making problem and statistical process control (SPC) charts turn out to be a powerful tool after the major complexities of the observed data (e.g., time-varying distributions and serial correlation) are properly addressed. In the literature, several SPC charts have been developed for solving the DEDP problem, but they are designed for detecting a single disease. In practice, however, we are often concerned about multiple diseases (e.g., different cardiovascular diseases), and there are no existing SPC methods designed for detecting multiple diseases yet due to its complexity. In this paper, a new dynamic screening system (DySS) is proposed for detecting multiple diseases. The new method first quantifies a patient's risk to each disease in concern at the current observation time, and then compares the quantified risk pattern with the regular risk pattern of non-diseased people that is estimated from a training dataset by a flexible longitudinal data modelling approach. The cumulative difference between the two risk patterns by the current observation time is used for determining whether a given patient has any of the multiple diseases in concern. Numerical studies show that the proposed method works well in different scenarios.

Dynamic screening system

Multiple diseases

Multivariate longitudinal data

Online process monitoring

Single-index model