Comparing Deterministic and Stochastic Reinforcement Learning for Glucose Regulation in Type 1 Diabetes...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan.
Type 1 Diabetes (T1D) is a chronic condition affecting millions worldwide, requiring external insulin administration to regulate blood glucose levels and prevent serious complications. Artificial Pancreas Systems (APS) for managing T1D currently rely on manual input, which adds a cognitive burden on...
| Publicado en: | Studies in Health Technology & Informatics Vol. 329; pp. 1039 - 1044 |
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| Autores principales: | , , |
| Formato: | algorithm proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187335015&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187335015 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 329 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 187335015 187335015 187335015 10.3233/SHTI250997 187335015 ppf: 1039 ppct: 5 formats: tig: atl: Comparing Deterministic and Stochastic Reinforcement Learning for Glucose Regulation in Type 1 Diabetes...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan. aug: au: TIMMS, David HETTIARACHCHI, Chirath SUOMINEN, Hanna affil: The Australian National University, Australia sug: subj: Prediction Models Learning Methods Blood Glucose Physiology Diabetes Mellitus, Type 1 Congresses and Conferences Taiwan Taiwan Human Funding Source Male Female Adolescence Adult Multimethod Studies Algorithms United States Food and Drug Administration Artificial Organs Pancreas Deep Learning Benchmarking Comparative Studies Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Type 1 Diabetes (T1D) is a chronic condition affecting millions worldwide, requiring external insulin administration to regulate blood glucose levels and prevent serious complications. Artificial Pancreas Systems (APS) for managing T1D currently rely on manual input, which adds a cognitive burden on people with T1D and their carers. Research into alleviating this burden through Reinforcement Learning (RL) explores enabling the APS to autonomously learn and adapt to the complex dynamics of blood glucose regulation, demonstrating improvements in in-silico evaluations compared to traditional clinical approaches. This evaluation study compared the primary polarities of RL for glucose regulation, namely, stochastic (e.g., Proximal Policy Optimization (PPO) and deterministic (e.g., Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms in-silico using quantitative and qualitative methods, patient specific clinical metrics, and the adult and adolescent cohorts of the U.S. Food and Drug Administration approved UVA/PADOVA 2008 model. Although the behavior of TD3 was easier to interpret, it did not typically outperform PPO, thereby challenging assessing their safety and suitability. This conclusion highlights the importance of improving RL algorithms in APS applications for both interpretability and predictive performance in future research. pubtype: Academic Journal doctype: algorithm proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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