Impact of Age Specialized Models for Hypoglycemia Classification

• URL: http://arxiv.org/abs/2604.23732v1
• Date: Sun, 26 Apr 2026 14:20:10 GMT
• Title: Impact of Age Specialized Models for Hypoglycemia Classification
• Abstract summary: We classify hypoglycemia 0, 5-15, 20-45, and 50-120 minutes before onset using DiaData.<n>Results show that a global population-based model yields similar or superior performance compared to age-segmented models.<n>These findings suggest that data from children, teenagers, and adults can be combined for training models on hypoglycemia classification.
• Score: 0.25884731694338026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored monitoring, care, and medication beyond standard clinical guidelines. Specifically, in autoimmune diseases like type 1 diabetes (T1D), where patients depend on exogenous insulin to compensate for insulin deficiency, medication dosing and the physiological response reflected in vital signs can differ. Insulin therapy can lead to hypoglycemia, a dangerous condition characterized by decreased blood glucose levels ($\leq$70). This risk can be mitigated through improved diabetes management supported by data analytics. Notably, leveraging data from continuous glucose monitoring (CGM) devices, hypoglycemia onset can be predicted. However, while glucose variability, auto-antibody levels, and hypoglycemia occurrence differ across age groups, hypoglycemia classification most often only relies on population-based models specialized in specific age ranges. In this work, we classify hypoglycemia 0, 5-15, 20-45, and 50-120 minutes before onset using DiaData, a large CGM dataset of patients with T1D ranging from children to seniors. In particular, we investigate: 1) the generalizability of a population-based model including all age groups, 2) the impact of age-segmented models trained separately per age group, and 3) the effect of model individualization through transfer learning. The results show that a global population-based model yields similar or superior performance compared to age-segmented models. These findings suggest that data from children, teenagers, and adults can be combined for training models on hypoglycemia classification. While glucose variation differs across age groups, short-term hypoglycemic patterns are similar. However, data of children obtain their best recall with age specialized model.

Related papers

• A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia [0.23031174164121124]
  Machine learning (ML) models can improve diabetes management by predicting hypoglycemia and providing optimal prevention methods.<n>This review investigates state-of-the-art models trained on data of continuous glucose monitoring (CGM) devices from patients with Type 1 Diabetes (T1D)<n>We compare the models' performance across short-term (15 to 120 min) and long term (3 to more than 24 hours) prediction horizons (PHs)
  arXiv  Detail & Related papers  (2026-01-09T23:06:36Z)
• Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation [51.12307713554633]
  TSODE is a safety-aware controller that integrates Thompson RL Sampling with a Neural Ordinary Differential Equation forecaster.<n>In the FDA-approved UVa/Padova simulator (adult cohort), TSODE achieved 87.9% time-in-range with less than 10% time below 70 mg/dL.
  arXiv  Detail & Related papers  (2025-11-16T02:11:33Z)
• Presenting DiaData for Research on Type 1 Diabetes [0.5250756556290761]
  This work systematically integrates 15 datasets to provide a large database of 2510 subjects with glucose measurements recorded every 5 minutes.<n>In total, 149 million measurements are included, of which 4% represent values in the hypoglycemic range.<n>The integrated dataset provides an equal distribution of sex and different age levels.
  arXiv  Detail & Related papers  (2025-08-05T14:29:43Z)
• Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers [44.735350554750916]
  We remotely recruited the largest dataset to date across the US to study insulin resistance.<n>Deep neural network models were developed to predict insulin resistance based on readily available digital and blood biomarkers.<n>Model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants.
  arXiv  Detail & Related papers  (2025-04-30T16:10:20Z)
• Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate [6.102406188211489]
  We introduce GLIMMER, a machine learning-based model for predicting blood glucose levels.<n>GLIMMER classifies glucose values into normal and abnormal ranges and employs a novel custom loss function.<n>These results represent a 23% improvement in RMSE and a 31% improvement in MAE compared to the best previously reported models.
  arXiv  Detail & Related papers  (2025-02-20T01:26:00Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes [44.077241051884926]
  We train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification.
  arXiv  Detail & Related papers  (2023-08-24T09:32:46Z)
• Modeling glycemia in humans by means of Grammatical Evolution [4.26706629463264]
  One of the main problems that arises in the (semi) automatic control of diabetes, is to get a model explaining how glycemia varies with insulin, food intakes and other factors. This paper proposes the application of evolutionary computation techniques to obtain customized models of patients.
  arXiv  Detail & Related papers  (2023-04-27T14:33:52Z)
• GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes [4.17510581764131]
  GLYFE is a benchmark of machine-learning-based glucose-predictive models. The results of nine different models coming from the glucose-prediction literature are presented.
  arXiv  Detail & Related papers  (2020-06-29T11:34:41Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.