The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multi-Step ahead Predictive Model for Blood Glucose Concentrations of Type-1 Diabetic Patients
Open accessPreprint — published as 10.1038/s41598-021-03341-5 · Paper page
Abstract
Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administrationand food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time stepsahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia.In this study, we present a novel multi-component deep learning model BG-Predict that predicts the BG levels in a multi-steplook ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients.For the prediction horizon (PH) of 30 minutes, the average values for root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andnormalized mean squared error (NRMSE) are 20.82±4.97 mg/dL, 15.04±3.70 mg/dL, 11.63±2.78 and 0.06±0.01 respectively. When Clarke and Parkes error grid analyses were performedcomparing predicted BG with actual BG, the results showed average percentage of points in Zone A of 83.88±6.84 and 87.44±4.97, respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Temporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238
Flagged first, then by citations
Metadata
- Authors
- Zaidi, S. M. A., Chandola, V., Ibrahim, M., Romanski, B., Mastrandrea, L. D., & Singh, T.
- Affiliations
- University at Buffalo, State University of New York (US)
- RWTH Aachen University (DE)
- Corresponding author
- Zaidi, Syed Mohammed Arshad
- Venue
- Scientific Reports
- Publisher
- Research Square Platform LLC
- Published
- 2021-03-24
- Volume
- 11
- Issue
- 1
- Pages
- 24332-24332
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8692478
- OpenAlex
- W4225966252
- Cited by
- 19
- References
- 1
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 6 · 2022: 2
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1910539, 1537210)
- Juvenile Diabetes Research Foundation International (1-SRA-2019-823-S-B)
- University at Buffalo
- Office of Advanced Cyberinfrastructure (OAC 1910539, 1910539)
- Division of Civil, Mechanical and Manufacturing Innovation (CMMI- 1537210.)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- type 1 diabetes, blood glucose prediction, multi-step forecasting, continuous glucose monitoring, deep learning, hypoglycemia, hyperglycemia, Clarke error grid, insulin management
- MeSH terms
- Deep Learning, Algorithms, Blood Glucose, Diabetes Mellitus, Type 1, Humans, Blood Glucose Self-Monitoring, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
