NDLI: Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support

Please wait, while we are loading the content...

Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support

Content Provider	Scilit
Author	Brown, Daniel Aldea, Arantza Harrison, Rachel Martin, Clare Bayley, Ian
Copyright Year	2018
Description	Journal: Artificial intelligence in medicine Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this process bolus calculators have been developed that suggest the appropriate dose. However these calculators do not automatically adapt to the specific circumstances of an individual and require fine-tuning of parameters, a process that often requires the input of an expert. To overcome the limitations of the traditional methods this paper proposes the use of an artificial intelligence technique, case-based reasoning, to personalise the bolus calculation. A novel aspect of our approach is the use of temporal sequences to take into account preceding events when recommending the bolus insulin doses rather than looking at events in isolation. The in silico results described in this paper show that given the initial conditions of the patient, the temporal retrieval algorithm identifies the most suitable case for reuse. Additionally through insulin-on-board adaptation and postprandial revision, the approach is able to learn and improve bolus predictions, reducing the blood glucose risk index by up to 27% after three revisions of a bolus solution.
Related Links	https://radar.brookes.ac.uk/radar/file/f440600a-77b9-45aa-8301-7f2d833396fd/1/fulltext.pdf
Ending Page	42
Page Count	15
Starting Page	28
ISSN	09333657
e-ISSN	18732860
DOI	10.1016/j.artmed.2017.09.007
Journal	Artificial intelligence in medicine
Volume Number	85
Language	English
Publisher	Elsevier BV
Publisher Date	2018-04-01
Access Restriction	Open
Subject Keyword	Journal: Artificial intelligence in medicine Medical Informatics Case-based Reasoning Feature Selection Knowledge Based Systems Similarity Measures
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Medicine

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in