arXiv Machine Learning

Subject-Conditioned Glucose Forecasting in Type-1 Diabetes

arXiv:2607. 19006v1 Announce Type: new Abstract: Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions.

arXiv Machine Learning

Jun 8

GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting

arXiv:2606. 06881v1 Announce Type: new Abstract: Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interventions, support automated insulin delivery, and reduce the risk of hypo- and hyperglycemic events.

By Baiying Lu, Zhaohui Liang, Ryan Pontius, Shengpu Tang, Temiloluwa Prioleau

llms benchmarks

arXiv Machine Learning

Jul 21

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

arXiv:2601. 05353v2 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk.

By Shovito Barua Soumma, Hassan Ghasemzadeh

llms rag agents

arXiv Machine Learning

Jun 5

Evidence-Guided Neural Architecture Selection under Uncertainty for Subject-Specific Blood Glucose Forecasting

arXiv:2606. 05373v1 Announce Type: new Abstract: Reliable neural architecture selection is an open challenge in time-series forecasting under limited, noisy, and heterogeneous data, where standard heuristic architecture design and validation approaches fail to ensure accurate and reliable prediction and generalization.

By Md Azharul Islam, Dwyer Deighan, Tarunraj Singha, Danial Faghihi

arXiv Machine Learning

Jun 18

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes

arXiv:2606. 18640v1 Announce Type: new Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes.

By Nathaniel Jeffries, Miriam Wolff, Sam Royston, Elizabeth Healey, Caleb Mayer, David Klonoff, Michael Snyder, Tao Wang

multimodal benchmarks

arXiv AI

Jun 12

LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data

arXiv:2606. 12699v1 Announce Type: cross Abstract: Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care.

By Yifan Gao, Yanmin Gong, Yun Shi, Yuanxiong Guo

llms benchmarks

arXiv Machine Learning

Jun 10

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

arXiv:2503. 19158v3 Announce Type: replace Abstract: Type 1 Diabetes (T1D) management is a complex task due to many variability factors.

By Stefano De Carli, Nicola Licini, Davide Previtali, Fabio Previdi, Antonio Ferramosca

arXiv AI

Jul 24

GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

arXiv:2607. 21117v1 Announce Type: cross Abstract: Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes.

By Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato, Paolo Napoletano

benchmarks safety

arXiv AI

2d ago

From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent

arXiv:2608. 13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals.

By Mingyu Huang, Weiqing Min, Ying Jin, Yilin Wang, Shuqiang Jiang

llms agents

arXiv Machine Learning

Jun 30

Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization

arXiv:2606. 29386v1 Announce Type: new Abstract: Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series extrapolation.

By Amirreza Dolatpour Fathkouhi, Justin Lee, Heman Shakeri

rag diffusion

arXiv AI

Jun 2

Towards a General Intelligence and Interface for Wearable Health Data

arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.

By Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari, Samy Abdel-Ghaffar, Marius Guerard, Kara Vaillancourt, Zhihan Zhang, Jake Garrison, Levi Albuquerque, Dimitris Spathis, Hong Yu, Hamid Palangi, Xuhai "Orson" Xu, David G. T. Barrett, Joseph Breda, Jed McGiffin, Yubin Kim, Yuwei Zhang, Naghmeh Rezaei, Samuel Solomon, Karan Ahuja, Tim Althoff, Jake Sunshine, Ming-Zher Poh, Benjamin Yetton, Ari Winbush, Nicholas B. Allen, James M. Rehg, Isaac Galatzer-Levy, Yun Liu, John Hernandez, Anupam Pathak, Conor Heneghan, Yuzhe Yang, Ahmed A. Metwally, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Xin Liu, Daniel McDuff

llms rag agents

arXiv AI

Aug 5

An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness

arXiv:2604. 23954v2 Announce Type: replace Abstract: Artificial Intelligence (AI) and Machine Learning (ML) models used in clinical settings are increasingly deployed to support clinical decision-making.

By Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque, Carlos Castillo

safety

arXiv Machine Learning

Jul 7

CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space

arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.

By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian

llms diffusion benchmarks

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