Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such models in clinical practice is slow, largely due to the lack of uncertainty quantification of provided predictions. In this work, we propose to model the future glucose trajectory conditioned on the past as an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.). This change allows us to learn the uncertainty and predict more accurately in the cases when the trajectory has a heterogeneous or multi-modal distribution. To estimate the parameters of the predictive distribution, we utilize the Transformer architecture. We empirically demonstrate the superiority of our method over existing state-of-the-art techniques both in terms of accuracy and uncertainty on the synthetic and benchmark glucose data sets.
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis
Weizmann Institute of Science（魏茨曼科学研究所）；NVIDIA（英伟达公司）；Tel Aviv University（特拉维夫大学）；Schneider Children’s Medical Center of Israel（以色列施耐德儿童医疗中心）；University of Copenhagen（哥本哈根大学）；Massachusetts General Hospital（麻省总医院）；Garvan Institute of Medical Research（加文医学研究所）；University of NSW（新南威尔士大学）；St Vincent’s Hospital（圣文森特医院）；University of Zurich（苏黎世大学）；University of Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
CNRS-LIMSI（法国国家科学研究中心-LIMSI）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris 8（巴黎第八大学）；Samovar；CNRS（法国国家科学研究中心）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系统与网络安全系）；School of Engineering Medicine, Texas A&M University（德克萨斯农工大学工程医学院）；Department of Family and Community Medicine, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校家庭与社区医学系）
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning
East China University of Science and Technology（华东理工大学）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；Shanghai Diabetes Institute（上海市糖尿病研究所）；Shanghai Clinical Center for Diabetes（上海市糖尿病临床医学中心）
DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
Institute of Data Science（数据科学研究所）；Saw Swee Hock School of Public Health, National University of Singapore, Singapore（Saw Swee Hock公共卫生学院，新加坡国立大学，新加坡）；National University Hospital, Singapore（新加坡国立医院）
