Transfer learning with randomized controlled trial data for postprandial glucose prediction

In recent years, numerous methods have been introduced to predict glucose levels using machine-learning techniques on patients’ daily behavioral and continuous glucose data. Nevertheless, a definitive consensus remains elusive regarding modeling the combined effects of diet and exercise for optimal glucose prediction. A notable challenge is the propensity for observational patient datasets from uncontrolled environments to overfit due to skewed feature distributions of target behaviors; for instance, diabetic patients seldom engage in high-intensity exercise post-meal. In this study, we introduce a unique Bayesian transfer learning framework using randomized controlled trial (RCT) data, primarily targeting postprandial glucose prediction. Initially, we gathered balanced training data from RCTs on healthy participants by randomizing behavioral conditions. Subsequently, we pretrained the model’s parameter distribution using RCT data from the healthy cohort. This pretrained distribution was then adjusted, transferred, and utilized to determine the model parameters for each patient. Our framework’s efficacy was appraised using data from 68 gestational diabetes mellitus patients in uncontrolled settings. The evaluation underscored the enhanced performance attained through our framework. Furthermore, when modeling the joint impact of diet and exercise, the synergetic model proved more precise than its additive counterpart. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT04714762 (ClinicalTrials.gov Identifier) ### Clinical Protocols ### Funding Statement Yes ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Committee of the Helsinki and Uusimaa Hospital District I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data cannot be shared publicly because of the terms of personal information protection in our trials. Data are available from the HUS Institutional Data Access / Ethics Committee (contact via Saila Koivosalo saila.koivusalo{at}hus.fi) for researchers who meet the criteria for access to confidential data.