Personalised Basal Tuner — Experiment 3 (Recommender Evaluation, In Silico)
Gasca Garcia, Daniel;
Personalised Basal Tuner — Experiment 3 (Recommender Evaluation, In Silico)
End-to-end pipeline (InVivo→InSilico→Meals→Metrics) to generate basal rate recommendations and validate them in-silico with Py-mgipsim. Reproducible runs, per-day scenarios, ISO plots and KPIs.
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- University of Manchester United Kingdom
CGM, In silico, Type 1 Diabetes, Basal insulin, Py-mgipsim, Recommender system
CGM, In silico, Type 1 Diabetes, Basal insulin, Py-mgipsim, Recommender system
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
Average
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Average