Bayesian cross-individual transfer learning for optimising predictive ratio CUSUM monitoring scheme.
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| Title: | Bayesian cross-individual transfer learning for optimising predictive ratio CUSUM monitoring scheme. |
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| Authors: | Chen, Ziwei1 (AUTHOR), Yu, Jiao1 (AUTHOR), Wu, Chunjie1 (AUTHOR) wumaths@mail.shufe.edu.cn, Tsung, Fugee2,3 (AUTHOR) |
| Source: | International Journal of Production Research. Jun2026, Vol. 64 Issue 11, p4471-4495. 25p. |
| Subjects: | Bayesian analysis, Statistical process control, Prediabetic state, Machine learning, Wearable technology, Continuous glucose monitoring, Change-point problems |
| Abstract: | 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 However, obtaining sufficient historical reference glucose data for individuals with pre-diabetes in Phase I is challenging, which leads to high false alarm rates with traditional control charts due to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] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194490056 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian cross-individual transfer learning for optimising predictive ratio CUSUM monitoring scheme. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Ziwei%22">Chen, Ziwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Jiao%22">Yu, Jiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Chunjie%22">Wu, Chunjie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wumaths@mail.shufe.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tsung%2C+Fugee%22">Tsung, Fugee</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jun2026, Vol. 64 Issue 11, p4471-4495. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink><br /><searchLink fieldCode="DE" term="%22Prediabetic+state%22">Prediabetic state</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+glucose+monitoring%22">Continuous glucose monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194490056 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2025.2605082 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 4471 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Statistical process control Type: general – SubjectFull: Prediabetic state Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Wearable technology Type: general – SubjectFull: Continuous glucose monitoring Type: general – SubjectFull: Change-point problems Type: general Titles: – TitleFull: Bayesian cross-individual transfer learning for optimising predictive ratio CUSUM monitoring scheme. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Ziwei – PersonEntity: Name: NameFull: Yu, Jiao – PersonEntity: Name: NameFull: Wu, Chunjie – PersonEntity: Name: NameFull: Tsung, Fugee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 64 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
