mlsanityData to 7 Oct 2026

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

arXiv 2304.06506 ↗First posted 4 Apr 2023cs.CY cs.AI cs.HCcited by 15

Not on the map. Why?

Abstract (arXiv)

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 3 of its 73 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

3 of 73 references.

References · 3

  1. 2018Convolutional Recurrent Neural Networks for Glucose Predictioncited by 20
  2. 2020Adherence to Personal Health Devices: A Case Study in Diabetes Managementcited by 5
  3. 2020Understanding Reflection Needs for Personal Health Data in Diabetescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 9.

15 papers.

Sort:

  1. 2026MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Managementcited by 4
  2. 2025Presenting DiaData for Research on Type 1 Diabetescited by 3
  3. 2025Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetescited by 2
  4. 2025BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatchescited by 2
  5. 2025Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaDatacited by 2
  6. 2025Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutionscited by 1
  7. 2026Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Modelscited by 1
  8. 2024Optimizing the Design of an Artificial Pancreas to Improve Diabetes Managementcited by 0
  9. 2026A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemiacited by 0
  10. 2026Impact of Age Specialized Models for Hypoglycemia Classificationcited by 0
  11. 2026GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecastingcited by 0
  12. 2026Personal Care Utility: Health as Everyday Infrastructurecited by 0
3 more
  1. 2026MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetescited by 0
  2. 2026Subject-Conditioned Glucose Forecasting in Type-1 Diabetescited by 0
  3. 2026GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetescited by 0