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arXiv Machine Learning By Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu, Eran Segal, Ahmed Metwally, Imran Razzak

GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer

Read the original on arXiv Machine Learning →

GlucoPRISM introduces a protocol‑supervised pretraining approach that separates glucose monitoring data into distinct Trait, State, and Sensor representation bands. By removing the Sensor band from a frozen encoder, the method improves cross‑cohort transfer performance, raising mean AUROC from 68.75 to 73.57 across twelve directions and outperforming the baseline GlucoFM model. The retained bands maintain device decodability while enhancing predictive accuracy on multiple public tasks and clinical endpoints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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