ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2018
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
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Who cites it
14 citing papers in PubMed.
- Article
- Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning.iScience · 2026Article
- Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- Forecasting air passenger traffic and market share using deep neural networks with multiple inputs and outputs.Frontiers in artificial intelligence · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- Chinese diabetes datasets for data-driven machine learning.Scientific data · 2023Article
- Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions.Sensors (Basel, Switzerland) · 2021Article
- Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models.Sensors (Basel, Switzerland) · 2021Article
- Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction.BMC medical informatics and decision making · 2021Article
- A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.ACM transactions on computing for healthcare · 2021Article
- Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data.Proceedings of machine learning research · 2020Article
- Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.Sensors (Basel, Switzerland) · 2020Article
- New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.G3 (Bethesda, Md.) · 2019Article
- Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.G3 (Bethesda, Md.) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall trajectory. This requires multi-step forecasting, a forecasting variant where one aims to predict multiple values in the future simultaneously. Standard methods to accomplish this can propagate error from prediction to prediction, reducing quality over the long term. In light of these challenges, we propose multi-output deep architectures for multi-step forecasting in which we explicitly model the distribution of future values of the signal over a prediction horizon. We apply these techniques to the challenging and clinically relevant task of blood glucose forecasting. Through a series of experiments on a real-world dataset consisting of 550K blood glucose measurements, we demonstrate the effectiveness of our proposed approaches in capturing the underlying signal dynamics. Compared to existing shallow and deep methods, we find that our proposed approaches improve performance individually and capture complementary information, leading to a large improvement over the baseline when combined (4.87
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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.