ArticleJournal of diabetes science and technology2013
Signal processing algorithms implementing the "smart sensor" concept to improve continuous glucose monitoring in diabetes.
Article in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Double-Layer Simplified Complex Interval Neural Network Stacking for Blood Glucose Prediction of Continuous Glucose Monitoring System.Biosensors · 2025Article
- Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia.NPJ digital medicine · 2025Article
- Digital Twins in Healthcare: Methodological Challenges and Opportunities.Journal of personalized medicine · 2023Review
- Reduction of measurement noise in a continuous glucose monitor by coating the sensor with a zwitterionic polymer.Nature biomedical engineering · 2018Article
- Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges.Sensors (Basel, Switzerland) · 2016Review
- Physical activity and type 1 diabetes: time for a rewire?Journal of diabetes science and technology · 2015Review
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
No grant is acknowledged in the PubMed record.
Abstract
Glucose readings provided by current continuous glucose monitoring (CGM) devices still suffer from accuracy and precision issues. In April 2013, we proposed a new conceptual architecture to deal with these problems and render CGM sensors algorithmically smarter, which consists of three modules for denoising, enhancement, and prediction placed in cascade to a commercial CGM sensor. The architecture was assessed on a data set consisting of 24 type 1 diabetes patients collected in four clinical centers of the AP@home Consortium (a European project of 7th Framework Programme funded by the European Committee). This article, as a companion to our prior publication, illustrates the technical details of the algorithms and of the implementation issues.
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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.