Treffer: Bayesian cross-individual transfer learning for optimising predictive ratio CUSUM monitoring scheme.

Recent advancements in wearable devices have led to research in diagnosing and identifying pre-diabetes and diabetes based on analysing continuous glucose monitoring data. Statistical process control is a widely used and valuable monitoring technique for detecting variability magnitudes of a continuous monitoring process. However, obtaining sufficient historical reference glucose data for individuals with pre-diabetes in Phase I is challenging, which leads to high false alarm rates with traditional control charts due to misidentifying in-control and out-of-control patterns. This paper introduces a novel Bayesian cross-individual process monitoring approach utilising transfer learning techniques to enhance the performance of the predictive ratio CUSUM control chart. Our proposed method showcases enhanced robustness in both simulated and real-world scenarios. Additionally, we provide practical guidance on assessing similarities between medical metrics and offer insights on fine-tuning hyperparameters and expanding volatility metrics based on simulation and practical results. The monitoring approach is universal and can be flexibly applied beyond the medical field to the industrial field. [ABSTRACT FROM AUTHOR]

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