Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
Abstract
Objective: This paper aims at proposing a new machine-learning based model to improve the calculation of mealtime insulin boluses (MIB) in type 1 diabetes (T1D) therapy using continuous glucose monitoring (CGM) data. Indeed, MIB is still often computed through the standard formula (SF), which does not account for glucose rate-of-change (ΔG), causing critical hypo/hyperglycemic episodes. Methods: Four candidate models for MIB calculation, based on multiple linear regression (MLR) and least absolute shrinkage and selection operator (LASSO) are developed. The proposed models are assessed in silico, using the UVa/Padova T1D simulator, in different mealtime scenarios and compared to the SF and three ΔG-accounting variants proposed in the literature. An assessment on real data, by retrospectively analyzing 218 glycemic traces, is also performed. Results: All four tested models performed better than the existing techniques. LASSO regression with extended feature-set including quadratic terms (LASSOQ) produced the best results. In silico, LASSOQ reduced the error in estimating the optimal bolus to only 0.86 U (1.45 U of SF and 1.36-1.44 U of literature methods), as well as hypoglycemia incidence (from 44.41% of SF and 44.60-45.01% of literature methods, to 35.93%). Results are confirmed by the retrospective application to real data. Conclusion: New models to improve MIB calculation accounting for CGM-ΔG and easy-to-measure features can be developed within a machine learning framework. Particularly, in this paper, a new LASSOQ model was developed, which ensures better glycemic control than SF and other literature methods. Significance: MIB dosage with the proposed LASSOQ model can potentially reduce the risk of adverse events in T1D therapy.
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The new linear regression formula resulted in improved glycemic outcomes, i.e., lower blood glucose risk index values, than the standard formula.
“The results show that the new formula resulted in improved glycemic outcomes, i.e., lower BGRI values, than the SF.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [40]
Noaro et al. derived a new formula to calculate meal insulin doses using regularized multiple linear regression.
“In Noaro et al. [40], a new formula to calculate meal insulin doses was derived using regularized multiple linear regression.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [40]
The dataset for the linear regression model was split into independent training and test sets containing data from different subjects.
“The dataset was split into independent training and test sets, containing data from different subjects.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [40]
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Gayetri Devi, S., V, Singh, A. K., Pande, D. M., Rajasekaran, K., Tiwari, M., & Sangeetha, S. (2026) · International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11576987
Poojitha, C., Sai, P. T. N., & Mercy, B. (2026) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci71772.2026.11710799
Machine learning: Early disease diagnosis, prediction and prevention
Meo, S. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45973-3.00002-6
- Authors
- Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A.
- Affiliations
- University of Padua (IT)
- ORCID
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2021-01
- Volume
- 68
- Issue
- 1
- Pages
- 247-255
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W3036368325
- Semantic Scholar
- 9543e0f94e6a25c0cb9ddfe193df38cccc27381d
- Cited by
- 57
- References
- 40
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 8 · 2021: 11 · 2020: 1
- FWCI
- 6.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- Italian Minister for Education
- Dipartimenti di Eccellenza (232/2016)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, continuous glucose monitoring, LASSO, multiple linear regression, hypoglycemia, glycemic control, insulin dose
- MeSH terms
- Machine Learning, Blood Glucose, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Retrospective Studies, Blood Glucose Self-Monitoring
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