Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

Authors

Institutions

Publication Details

Journal
Journal of Diabetes Science and Technology
Published
2025-03-05
DOI
https://doi.org/10.1177/19322968251321508
Citations
1
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
1.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

Samantha Kleinberg, E.H.C. Choi, Yuan Shen
1 citations
Journal of Diabetes Science and Technology
Diabetes Management and Research
1.29
article

Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

Samantha Kleinberg, E.H.C. Choi, Yuan Shen
article en
1 citations

Abstract

We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

Journal of Diabetes Science and Technology
Stevens Institute of Technology (US)
National Science Foundation, National Center for Advancing Translational Sciences
Openalex Percentile: Top 22%
Diabetes Management and Research
1.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.