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June 7, 2026Diabetes

2321-P: Machine-Learning Modeling for T2DM Prediction in over 3 Million Adults

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Population

3,365,464 adults aged 18-70 years receiving care at Kaiser Permanente Northern California from 2012-2024…

Design

Cohort

Follow-up

median 5.4 years

Authors

LRLUIS A. RODRIGUEZMYMaher YassinRNROMAIN NEUGEBAUER

Discussion

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Overview

An EHR-based machine-learning model demonstrated excellent discrimination and calibration for predicting 1-, 3-, and 10-year risk of incident T2DM in a large real-world cohort.

Structured PICO

P
Population
3,365,464 adults aged 18-70 years receiving care at Kaiser Permanente Northern California from 2012-2024, median age 39, 55% female.
I
Intervention
EHR-based machine-learning prediction model (hazard-based Super Learning approach)
O
Outcome
Incident T2DM at 1-, 3-, and 10-year follow-up

An EHR-based machine-learning model demonstrated excellent discrimination and calibration for predicting 1-, 3-, and 10-year risk of incident T2DM in a large real-world cohort.

Limitations

  • Lack of external validation (currently ongoing)

Cite This Study

RODRIGUEZ et al. (2026) studied this question.

synapsesocial.com/papers/6a250cbc7def13d035e1cf0ahttps://doi.org/10.2337/db26-2321-p
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 11136-OR: Longitudinal Time-to-Event Modeling and Identification of Risk Factors for the Occurrence of Microvascular Complications in Youth-Onset Type 2 Diabetes2026
  2. 22223-P: Harnessing Machine Learning to Forecast Two-Year Progression to Stage 3 Type 1 Diabetes (T1D) in Individuals with Multiple Type 1 Diabetes Autoantibodies: A Noise-Resistant, Multidimensional Clustering Approach2026
  3. 3A Machine Learning Model for Predicting the Risk of Developing Diabetes - T2DM Using Real-World Data from Kilifi, Kenya2025
  4. 42965-LB: Cardiovascular Disease Risk Prediction in Type 1 Diabetes over 30 Years2026
  5. 52222-P: Prospective Validation of AI for Detecting Misclassified Adult Type 1 Diabetes: Insights on Precision, Clinical Workflows, and Adoption2026