ArticleJournal of healthcare informatics research2020
Predicting Glycaemia in Type 1 Diabetes Patients: Experiments in Feature Engineering and Data Imputation.
Article in Journal of healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 32 citations in OpenAlex.
- Metabolic Responses to Exercise and Nutritional Strategies in Type 1 Diabetes Using Automated Insulin Delivery Systems: A Narrative Review.Metabolites · 2026Review
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Personalised Blood Glucose Time Series Forecasting in Type 1 Diabetes: Deep Collaborative Adversarial Learning.Journal of personalized medicine · 2026Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis.Scientific reports · 2024Article
- Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.Sensors (Basel, Switzerland) · 2024Article
- Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network.Journal of diabetes science and technology · 2023Article
- Applying the use of shared medical appointments (SMAs) to improve continuous glucose monitor (CGM) use, glycemic control, and quality of life in marginalized youth with type 1 diabetes: Study protocol for a pilot prospective cohort study.Contemporary clinical trials communications · 2023Article
- Research on early warning of renal damage in hypertensive patients based on the stacking strategy.BMC medical informatics and decision making · 2022Article
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Neural Physiological Model: A Simple Module for Blood Glucose Prediction.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Homeostasis as a proportional-integral control system.NPJ digital medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with type 1 diabetes manually regulate blood glucose concentration by adjusting insulin dosage in response to factors such as carbohydrate intake and exercise intensity. Automated near-term prediction of blood glucose concentration is essential to prevent hyper- and hypoglycaemic events in type 1 diabetes patients and to improve control of blood glucose levels by physicians and patients. The imperfect nature of patient monitoring introduces missing values into all variables that play important roles to predict blood glucose level, necessitating data imputation. In this paper, we investigated the importance of variables and explored various feature engineering methods to predict blood glucose level. Next, we extended our work by developing a new empirical imputation method and investigating the predictive accuracy achieved under different methods to impute missing data. Also, we examined the influence of past signal values on the prediction of blood glucose levels. We reported the relative performance of predictive models in different testing scenarios and different imputation methods. Finally, we found an optimal combination of data imputation methods and built an ensemble model for the reliable prediction of blood glucose levels on a 30-minute horizon.
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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.