Shortcomings in the Evaluation of Blood Glucose Forecasting.

ABSTRACT:

Objective

Recent years have seen an increase in machine learning (ML)-based blood glucose (BG) forecasting models, with a growing emphasis on potential application to hybrid or closed-loop predictive glucose controllers. However, current approaches focus on evaluating the accuracy of these models using benchmark data generated under the behavior policy, which may differ significantly from the data the model may encounter in a control setting. This study challenges the efficacy of such evaluation approaches, demonstrating that they can fail to accurately capture an ML-based model's true performance in closed-loop control settings.

Methods

Forecast error measured using current evaluation approaches was compared to the control performance of two forecasters - a ML-based model (

SUBMITTER: Lee JM 

PROVIDER: S-EPMC11724010 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature