Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management

Aug 2, 2026

Honeymoon phase in pediatric type 1 diabetes (T1D) brings temporary improvement in glycemic control while increasing complexity in insulin management. A machine learning (ML)-driven framework was developed to detect this phase accurately and use the result to optimize insulin therapy and reduce adverse outcomes. Pediatric data (ages 6–17) with continuous glucose monitoring, GMI reports, HbA1c, and medical history were used to train models including LSTM, transformers, random forest, and gradient boosting machines. The transformer model achieved 91% accuracy, supporting more personalized insulin adjustments and safer glucose control.

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Pages10

Reading Time25 min

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Abstract

Introduction

Methods

Data and ML models

Results

Conclusion

Keywords

J Clin Res Pediatr Endocrinol 2025;17(3):278-287 Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management Satheeskumar R. Narasaraopeta Engineering College, Department of Computer Science and Engineering, Andhra Pradesh, India | What is already known on this topic?
The honeymoon phase in type 1 diabetes (T1D) is characterized by a temporary period of reduced insulin needs and better glucose control. Current methods for identifying this phase rely on clinical observations, but they lack precision and often result in delayed or suboptimal insulin management.
What this study adds?
This study introduces advanced machine learning models, such as long short-term memory networks and transformer models, to accurately detect the honeymoon phase in T1D patients. By analyzing continuous glucose monitoring data, these models enhance the precision of honeymoon phase identification, leading to more personalized insulin management and improved overall glycemic control. | | | --- | --- | | Abstract
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 shortterm 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.
Keywords: Honeymoon phase, insulin management, machine learning, type 1 diabetes | | | Introduction
Type 1 diabetes (T1D) is a chronic autoimmune condition characterized by the destruction of insulin-producing | beta cells in the pancreas, leading to lifelong dependence on exogenous insulin therapy (1,2) . The honeymoon phase is a well-recognized but transient period following the initial diagnosis of T1D, where patients experience a | | Cite this article as: Satheeskumar R. Machine learning-driven identification of the honeymoon phase in pediatric type 1 diabetes and optimizing insulin management. J Clin Res Pediatr Endocrinol. 2025;17(3):278-287 | | Address for Correspondence: Satheeskumar R. Prof. , Narasaraopeta Engineering College, Department of Computer Science and Engineering, Andhra Pradesh, India E-mail: satheesme@gmail.com ORCID: [orcid.org/0000-0002-4642-7339](orcid.org/0000-0002-4642-7339) Conflict of interest: None declared Received: 29.08.2024 Accepted: 07.01.2025 Epub: 23.01.2025 Publication date: 22.08.2025 ©Copyright 2025 by Turkish Society for Pediatric Endocrinology and Diabetes / The Journal of Clinical Research in Pediatric Endocrinology published by Galenos Publishing House. Licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) International License. temporary remission of symptoms and improv

FAQs

What problem does the study address in pediatric type 1 diabetes?

The study targets the difficulty of accurately identifying the honeymoon phase, which can lead to imprecise insulin adjustments and higher risks of hypoglycemia or hyperglycemia.

Which machine learning models were used to detect the honeymoon phase?

Models included LSTM networks, transformer models, random forest, and gradient boosting machines (GBMs), trained using continuous glucose monitoring and related clinical inputs.

How did the study evaluate performance and what were the best results?

Model performance was measured by accuracy, with the transformer model reaching the highest accuracy at 91%, followed by GBMs at 89% and LSTM at 88%.

Aug 2, 2026

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