ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Neural Physiological Model: A Simple Module for Blood Glucose Prediction.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Continuous glucose monitors (CGM) and insulin pumps are becoming increasingly important in diabetes management. Additionally, data streams from these devices enable the prospect of accurate blood glucose prediction to support patients in preventing adverse glycemic events. In this paper, we present Neural Physiological Encoder (NPE), a simple module that leverages decomposed convolutional filters to automatically generate effective features that can be used with a downstream neural network for blood glucose prediction. To our knowledge, this is the first work to investigate a decomposed architecture in the diabetes domain. Our experimental results show that the proposed NPE model can effectively capture temporal patterns and blood glucose associations with other daily activities. For predicting blood glucose 30-mins in advance, NPE+LSTM yields an average root mean square error (RMSE) of 9.18 mg/dL on an in-house diabetes dataset from 34 subjects. Additionally, it achieves state-of-the-art RMSE of 17.80 mg/dL on a publicly available diabetes dataset (OhioT1DM) from 6 subjects.
Indexed as
Identifiers
What 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.