Evidence map›Paper›PMID 35658633›Full record

ArticleJournal of diabetes science and technology2024

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location.

Aaron P Tucker, Arthur G Erdman, Pamela J Schreiner, Sisi Ma, Lisa S Chow

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Aaron P TuckerEarl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-1720-9543
Arthur G ErdmanEarl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.
Pamela J SchreinerDivision of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA.
Sisi MaDivision of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA.
Lisa S ChowDivision of Diabetes, Endocrinology and Metabolism, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-6210-2307
University of Minnesota · US

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
NCATS NIH HHS UL1 TR002494
6 · The paper itself

Abstract

backgroundContinuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN.

methodsIn response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location.

resultsWe observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes (

conclusionWe observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Indexed as

Diabetes Mellitus, Type 2GlucoseBlood GlucoseBlood Glucose Self-MonitoringForecastingHumansNeural Networks, ComputerBlood GlucoseGlucosecontinuous glucose monitoringgated recurrent unitglucose forecastneural network

Identifiers

PMID35658633
PMCPMC10899835
OpenAlexW4281827885

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.