Evidence map›Paper›PMID 28479659›Full record

ArticleProceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control2014

Design of an Artificial Pancreas using Zone Model Predictive Control with a Moving Horizon State Estimator.

Justin J Lee, Ravi Gondhalekar, Francis J Doyle

Abstract read
In one paragraph

Article in Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.Automatica : the journal of IFAC, the International Federation of Automatic Control · 2018
    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

3 authors.

Justin J LeeUniversity of California, Santa Barbara, CA, USA, and the Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Ravi GondhalekarUniversity of California, Santa Barbara, CA, USA, and the Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Francis J DoyleUniversity of California, Santa Barbara, CA, USA, and the Sansum Diabetes Research Institute, Santa Barbara, CA, USA.

Funding

Ambulatory Artificial Pancreas: merging physiology, behavior, and control designDP3DK094331 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BASU, ANANDA, DOYLE, FRANCIS J · 2011 to 2014
$4.6M
Closed-loop Artificial Pancreas: Algorithm Engineering and Clinical EvaluationR01DK085628 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DOYLE, FRANCIS J, JOVANOVIC, LOIS · 2009 to 2013
$3.4M
NIDDK NIH HHS DP3 DK094331NIDDK NIH HHS R01 DK085628
6 · The paper itself

Abstract

A zone model predictive control (zone-MPC) algorithm that utilizes the Moving Horizon State Estimator (MHSE) is presented. The control application is an artificial pancreas for treating people with type 1 diabetes mellitus. During the meal challenge, the prediction quality of the zone-MPC algorithm with the MHSE was significantly better than when using the current Luenberger observer to provide the state estimate. Consequently, the controller using the MHSE rejected the meal disturbance faster and without inducing extra hypoglycemia risk (e.g., lower postprandial blood glucose peak by 10 mg/dL and higher postprandial minimum blood glucose by 11 mg/dL). The faster rejection of the meal disturbance led to a longer time in the clinically accepted safe region (70-180 mg/dL) by 13%, and this may reduce the likelihood of the complications related to type 1 diabetes mellitus.

Identifiers

PMID28479659
PMCPMC5419692

What OpenQuestion holds

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Registered trials

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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.