Observational studyMedical & biological engineering & computing2019
Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters.
Observational study in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Pooled it
- A new multivariate blood glucose prediction method with hybrid feature clustering and online transfer learning.Health information science and systems · 2024Article
- Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.Journal of diabetes science and technology · 2024Review
- Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.Journal of diabetes science and technology · 2024Article
- Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients.Sensors (Basel, Switzerland) · 2023Article
- Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia.Frontiers in nutrition · 2022Review
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept.Medical & biological engineering & computing · 2019Article
- Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors.Sensors (Basel, Switzerland) · 2019Article
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL
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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.