Application of Bayesian control charts with dynamic adjustment for risk monitoring

Le résumé fourni par la source

Monitoring changes in the occurrence probability of target events is crucial in many fields, such as healthcare and industrial systems, to ensure timely and effective management decisions. Risk-adjusted models, which incorporate covariate information, are commonly used to estimate these probabilities. However, when actual risk increases, relying solely on event outcomes may delay the detection of potential changes, affecting management decisions. To address this issue, various risk-adjusted control charts have been proposed. Most existing schemes focus on monitoring changes in risk-adjusted model coefficients (e.g., slope and intercept), which may limit their flexibility when risk fluctuations deviate from parametric assumptions. In this study, we propose a risk-adjusted exponentially weighted moving average control chart based on a Bayesian framework. By integrating risk-adjusted expected values as prior information, this scheme directly monitors risk levels, providing a more intuitive and flexible detection mechanism. This newly proposed control chart is designed to monitor small shifts in the failure rate of a Bernoulli distribution. Monte Carlo simulations demonstrate that the proposed method outperforms the existing charts in monitoring small and moderate shifts. Finally, two real-world case studies on ICU patient monitoring and displacement monitoring are used to illustrate the practical applicability of the proposed approach.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.