Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management
Objective: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent...
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Galenos Yayincilik
2025-09-01
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| Series: | JCRPE |
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| Online Access: | https://www.jcrpe.org/articles/machine-learning-driven-identification-of-the-honeymoon-phase-in-pediatric-type-1-diabetes-and-optimizing-insulin-management/doi/jcrpe.galenos.2025.2024-8-13 |
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| author | Satheeskumar R. |
| author_facet | Satheeskumar R. |
| author_sort | Satheeskumar R. |
| collection | DOAJ |
| description | Objective: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes.
Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring data, glucose management indicator (GMI) reports, hemoglobin A1c (HbA1c) values, and patient medical history, were used to train ML models including long short-term memory (LSTM) networks, transformer models, random forest, and gradient boosting machines (GBMs). These were designed to analyze glucose trends and identify the honeymoon phase in T1D patients.
Results: The transformer model achieved the highest accuracy at 91%, followed by GBMs at 89%, LSTM at 88%, and random forest at 87%. Key features, such as glucose variability, insulin adjustments, GMI values, and HbA1c levels were critical to model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk.
Conclusion: The ML-driven approach provides a robust method for detecting the honeymoon phase in T1D patients, demonstrating potential for improved personalized insulin management. The findings suggest significant benefits in patient outcomes, with future research focused on further validation and clinical integration. |
| format | Article |
| id | doaj-art-2ca77f25b9e342d79bf1414164fb1d95 |
| institution | Kabale University |
| issn | 1308-5727 1308-5735 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Galenos Yayincilik |
| record_format | Article |
| series | JCRPE |
| spelling | doaj-art-2ca77f25b9e342d79bf1414164fb1d952025-08-25T06:17:48ZengGalenos YayincilikJCRPE1308-57271308-57352025-09-0117327828710.4274/jcrpe.galenos.2025.2024-8-13Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin ManagementSatheeskumar R.0https://orcid.org/0000-0002-4642-7339Narasaraopeta Engineering College Department of Computer Science and Engineering, Andhra Pradesh, IndiaObjective: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes. Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring data, glucose management indicator (GMI) reports, hemoglobin A1c (HbA1c) values, and patient medical history, were used to train ML models including long short-term memory (LSTM) networks, transformer models, random forest, and gradient boosting machines (GBMs). These were designed to analyze glucose trends and identify the honeymoon phase in T1D patients. Results: The transformer model achieved the highest accuracy at 91%, followed by GBMs at 89%, LSTM at 88%, and random forest at 87%. Key features, such as glucose variability, insulin adjustments, GMI values, and HbA1c levels were critical to model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk. Conclusion: The ML-driven approach provides a robust method for detecting the honeymoon phase in T1D patients, demonstrating potential for improved personalized insulin management. The findings suggest significant benefits in patient outcomes, with future research focused on further validation and clinical integration.https://www.jcrpe.org/articles/machine-learning-driven-identification-of-the-honeymoon-phase-in-pediatric-type-1-diabetes-and-optimizing-insulin-management/doi/jcrpe.galenos.2025.2024-8-13honeymoon phaseinsulin managementmachine learningtype 1 diabetes |
| spellingShingle | Satheeskumar R. Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management JCRPE honeymoon phase insulin management machine learning type 1 diabetes |
| title | Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management |
| title_full | Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management |
| title_fullStr | Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management |
| title_full_unstemmed | Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management |
| title_short | Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management |
| title_sort | machine learning driven identification of the honeymoon phase in pediatric type 1 diabetes and optimizing insulin management |
| topic | honeymoon phase insulin management machine learning type 1 diabetes |
| url | https://www.jcrpe.org/articles/machine-learning-driven-identification-of-the-honeymoon-phase-in-pediatric-type-1-diabetes-and-optimizing-insulin-management/doi/jcrpe.galenos.2025.2024-8-13 |
| work_keys_str_mv | AT satheeskumarr machinelearningdrivenidentificationofthehoneymoonphaseinpediatrictype1diabetesandoptimizinginsulinmanagement |