Evidence map›Paper›PMID 33762804›Full record

ArticleIEEE transactions on control systems technology : a publication of the IEEE Control Systems Society2020

Embedded Model Predictive Control for a Wearable Artificial Pancreas.

Ankush Chakrabarty, Elizabeth Healey, Dawei Shi, Stamatina Zavitsanou, Francis J Doyle, Eyal Dassau

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

Article in IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

6 authors at 1 institution in 1 country.

Ankush ChakrabartyControl and Dynamical Systems Group, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA.
Elizabeth HealeyHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Dawei ShiHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Stamatina ZavitsanouHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Francis J DoyleHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Eyal DassauHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Harvard University · US

Funding

On-body ecosystem for automated insulin delivery in type 1 diabetesDP3DK113511 · NIDDK · YALE UNIVERSITY · PI DASSAU, EYAL, LAFFEL, LORI M · 2017 to 2017
$3.0M
Pediatric Artificial Pancreas System for Enhanced Diabetes Management in Young Children with Type 1 DiabetesDP3DK104057 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DASSAU, EYAL, DOYLE, FRANCIS J · 2014 to 2014
$1.8M
NIDDK NIH HHS DP3 DK104057NIDDK NIH HHS DP3 DK113511
6 · The paper itself

Abstract

While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop

Indexed as

Artificial pancreasbiomedical controlcontrol applicationsconvex optimizationembedded systemsmodel predictive controlsafety-critical control

Identifiers

PMID33762804
PMCPMC7983018
OpenAlexW2974412449

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.