机器学习驱动的儿童1型糖尿病蜜月期识别和优化胰岛素管理。

IF 1.5 4区 医学 Q4 ENDOCRINOLOGY & METABOLISM
Satheeskumar R
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引用次数: 0

摘要

目的:1型糖尿病(T1D)的蜜月期出现暂时的血糖控制改善,使胰岛素管理复杂化。本研究旨在开发和验证一种机器学习驱动的方法,以准确检测这一阶段,以优化胰岛素治疗并预防不良后果。方法:使用6-17岁儿童T1D患者的数据,包括连续血糖监测(CGM)数据、葡萄糖管理指标(GMI)报告、糖化血红蛋白(HbA1c)值和患者病史,训练机器学习模型。这些模型包括长短期记忆(LSTM)网络、Transformer模型、随机森林和梯度增强机,旨在分析糖尿病患者的血糖趋势并确定蜜月期。结果:Transformer模型的准确率最高,达到91%,其次是Gradient Boosting Machines(89%)、LSTM(88%)和Random Forest(87%)。血糖变异性、胰岛素调节、GMI值和HbA1c水平等关键特征对模型性能至关重要。蜜月期的准确识别可以优化胰岛素调节,加强血糖控制,降低低血糖风险。结论:机器学习驱动的方法为T1D患者的蜜月期检测提供了一种强大的方法,显示了改善个性化胰岛素管理的潜力。研究结果表明对患者预后有显著益处,未来的研究重点是进一步验证和临床整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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) presents a temporary improvement in glycemic control, complicating insulin management. This study aims to develop and validate a machine learning-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 (CGM) data, Glucose Management Indicator (GMI) reports, HbA1c values, and patient medical history, were used to train machine learning models. These models Long Short-Term Memory (LSTM) networks, Transformer models, Random Forest, and Gradient Boosting Machines 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 Gradient Boosting Machines 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 in model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk.

Conclusion: The machine learning-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.

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来源期刊
Journal of Clinical Research in Pediatric Endocrinology
Journal of Clinical Research in Pediatric Endocrinology ENDOCRINOLOGY & METABOLISM-PEDIATRICS
CiteScore
3.60
自引率
5.30%
发文量
73
审稿时长
20 weeks
期刊介绍: The Journal of Clinical Research in Pediatric Endocrinology (JCRPE) publishes original research articles, reviews, short communications, letters, case reports and other special features related to the field of pediatric endocrinology. JCRPE is published in English by the Turkish Pediatric Endocrinology and Diabetes Society quarterly (March, June, September, December). The target audience is physicians, researchers and other healthcare professionals in all areas of pediatric endocrinology.
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