ArticleJournal of medical systems2006
A fuzzy model of glucose regulation.
Article in Journal of medical systems, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Specification and Verification of Medical Monitoring System Using Petri-nets.Journal of medical signals and sensors · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a detailed glucose regulation model using fuzzy inference system (FIS) descriptions of hormonal control action and the familiar Michaelis-Menten (M-M) kinetic description for glucose transport. The fuzzy M-M model is compared and contrasted with a well-known comprehensive glucose model. The two models give similar results for glucose response, endogenous glucose production, and total uptake. The fuzzy M-M model features a renal subsystem that provides 25% of the endogenous glucose production. The work demonstrates the successful application of fuzzy logic and fuzzy inference to biological modelling. The flexibility of fuzzy inference, a linguistic description technique, permits conceptually simple statements about nonlinear processes.
Indexed as
Identifiers
16848132What 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.