Skip to main navigation Skip to search Skip to main content

Comparing artificial pancreas controlled by hybrid "closed-loop" machine learning (ML) trained algorithm to multi-daily injection (MDI), insulin pump without CGM and "sensor assisted" insulin pump therapies for Diabetes Type 1 (DT1) treatment

Research output: Book chapterConference contributionpeer-review

5 Citations (Scopus)

Abstract

Aims/hypothesis Diabetes Type 1 (DT1) therapy by means of artificial pancreas consisting on insulin pump with CGM and hybrid "closed-loop"control algorithm trained with machine learning technology provides better glycemia control than multi-daily injection, insulin pump without CGM and "sensor assisted"insulin pump therapies.Methods Using Accu-Chek smart pix software to analyze the own data collected in-vivo by JC Peiro, author and DT1 patient, in the period August 2004 to August 2019, collecting glycemia control data using the different therapies. Accu-Chek smart pix has been used to collect 4.621 glycemia tests for a period of 1.241 days. The period measured with CGM contains data with +90% sensor coverage. Control graphics measure mean and median glycemia, standard deviation, time in range %, above range%, below range %, % hypoglycemia, high glucose blood index and low glucose blood index. Finally, analysis is validated in-silico using the UVA/Padova T1DMS simulator, with MATLAB and a population of 30 individuals under Multi-daily Injection and under "closed-loop"artificial pancreas therapies.Results The improvement of therapy with artificial pancreas with hybrid "closed-loop"control algorithm, when comparing to MDI reduces-70.7% the periods above range, reduces-67.2% periods below range, TIR% increases 75% reaching 84%, hypoglycemia's % reduced-91.2%, HGBI reduces-67%, LGBI reduces-73.8% reaching 2.3 and 1.6 respectively, reducing the mean glycemia in-16% reaching mean of 121mg/dL with SD 42mg/dL, reducing median glycemia in-20% reaching median of 115 mg/dL with SD 55 mg/dL and reducing the Glycated Hemoglobin (HbA1c)-20.1% reaching a 6.6% value.Conclusions/interpretation We can conclude that therapy for DT1 using "hybrid closed-loop"artificial pancreas controlled by ML trained algorithm provides statistically better glycemic control results. The improvement of the treatment is significantly better regarding all the analyzed therapies: MDI, insulin pump without CGM and "sensor assisted"insulin pump.

Original languageEnglish
Title of host publication2020 International Conference on Data Analytics for Business and Industry
Subtitle of host publicationWay Towards a Sustainable Economy, ICDABI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728196756
DOIs
Publication statusPublished - 26 Oct 2020
Externally publishedYes
Event2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 - Sakheer, Bahrain
Duration: 26 Oct 202027 Oct 2020

Publication series

Name2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

Conference

Conference2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020
Country/TerritoryBahrain
CitySakheer
Period26/10/2027/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • artificial pancreas
  • CGM
  • closed-loop
  • hybrid closed-loop
  • machine learning
  • MDI
  • sensor assisted insulin pump

Fingerprint

Dive into the research topics of 'Comparing artificial pancreas controlled by hybrid "closed-loop" machine learning (ML) trained algorithm to multi-daily injection (MDI), insulin pump without CGM and "sensor assisted" insulin pump therapies for Diabetes Type 1 (DT1) treatment'. Together they form a unique fingerprint.

Cite this