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Insulin Recommender Systems for T1DM: A Review
Joaquim Massana1, Ferran Torrent-Fontbona2, Beatriz López2
1Polytechnical School, Universitat de Girona, Girona, Catalonia, Spain. joaquim.massana@udg.edu.
Advances in Experimental Medicine and Biology
|February 9, 2020
Summary
This review of 70 papers highlights a need for standardized methods in insulin recommender systems (IRSs) for type 1 diabetes mellitus. Current research often lacks real-world testing and detailed feature information, hindering progress.
Area of Science:
- Biomedical Engineering
- Endocrinology
- Artificial Intelligence in Medicine
Background:
- Type 1 diabetes mellitus (T1DM) necessitates external insulin management due to non-functional pancreatic beta-cells.
- Maintaining safe blood glucose (BG) levels is critical for individuals with T1DM.
- Insulin recommender systems (IRSs) aim to automate and optimize insulin delivery.
Purpose of the Study:
- To comprehensively review and summarize existing Insulin Recommender Systems (IRSs) for T1DM management.
- To analyze 70 published works based on key aspects of IRS development and implementation.
Main Methods:
- Systematic literature review of 70 research papers on IRSs.
- Analysis focused on recommendation technology, control procedures, hardware, testing, pricing, and outcomes.
- Comparative assessment of methodologies and reported results.
Main Results:
- Significant lack of published research combining real-world experimentation with simulation processes.
- Incomplete reporting of IRS features in a substantial number of publications.
- Variability in experimental design and result presentation impedes direct comparison of studies.
Conclusions:
- The field of IRSs for T1DM requires standardized protocols for experimentation and reporting.
- Establishing common standards is essential for robust comparison analysis and scientific advancement.
- Further research should prioritize transparent reporting and integrated testing methodologies.
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