ArticleCEUR workshop proceedings2020
The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020.
Article in CEUR workshop proceedings, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
81 citing papers in PubMed.
- Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance.PLOS digital health · 2026Article
- Causal mediation pathways in continuous postprandial glucose monitoring for type 1 diabetes patients.npj metabolic health and disease · 2026Article
- Article
- Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.JMIR diabetes · 2026Article
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning.iScience · 2026Article
- Enhanced glucose forecasting using recurrent neural network and advanced feature engineering.Scientific reports · 2026Article
- Personalised Blood Glucose Time Series Forecasting in Type 1 Diabetes: Deep Collaborative Adversarial Learning.Journal of personalized medicine · 2026Article
- Causal Mediation Pathways in Continuous Postprandial Glucose Monitoring for Type 1 Diabetes Patients.Research square · 2026Article
- Causal Mediation Pathways in Continuous Postprandial Glucose Monitoring for Type 1 Diabetes Patients.medRxiv : the preprint server for health sciences · 2026Article
- Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes.Smart health (Amsterdam, Netherlands) · 2026Article
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.Scientific reports · 2026Article
- Comprehensive blood glucose level prediction from HbAScientific reports · 2026Article
- Sugar slay: a gamified decision support ecosystem for type 1 diabetes.Frontiers in digital health · 2026Article
- Machine Learning for Sensor Analytics: A Comprehensive Review and Benchmark of Boosting Algorithms in Healthcare, Environmental, and Energy Applications.Sensors (Basel, Switzerland) · 2025Review
- Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study.JMIR diabetes · 2025Article
- Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses: Development of a Clustering Algorithm.JMIR diabetes · 2025Article
- A multimodal physiological dataset for non-invasive blood glucose estimation.Scientific data · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This paper documents the OhioT1DM Dataset, which was developed to promote and facilitate research in blood glucose level prediction. It contains eight weeks' worth of continuous glucose monitoring, insulin, physiological sensor, and self-reported life-event data for each of 12 people with type 1 diabetes. An associated graphical software tool allows researchers to visualize the integrated data. The paper details the contents and format of the dataset and tells interested researchers how to obtain it. The OhioT1DM Dataset was first released in 2018 for the first Blood Glucose Level Prediction (BGLP) Challenge. At that time, the dataset was half its current size, containing data for only six people with type 1 diabetes. Data for an additional six people is being released in 2020 for the second BGLP Challenge. This paper subsumes and supersedes the paper which documented the original dataset.
Identifiers
33584164PMC7881904What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.