arXiv Machine Learning October 22, 2026 By Shakson Isaac, Chirag Patel

SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management

Read the original on arXiv Machine Learning →

SSMCGM-Stream is a streaming state‑space model designed for personalized diabetes management. It updates a hidden state with each new glucose reading, enabling constant‑time forecasting, physiologically structured counterfactual reasoning, and interpretable forecasts via hidden‑state attribution. The model, evaluated on the T1DEXI cohort, matches strong neural baselines in accuracy while significantly lowering computational cost, making continuous on‑device decision support feasible.

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.

agents

arXiv Machine Learning

Jul 22

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.

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

multimodal 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 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 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 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 Computation and Language

Sep 1

DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening

arXiv:2608.31128v1 Announce Type: new Abstract: Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and ci...

By Yung Wei Shueh, Zhi-Jie Chen, Chia-Hsuan Hsu, Hsin-Ling Hsu, Donghua Zhang, Chenwei Wu, Jun-En Ding, Tongze Zhang, Shihao Yang, Pengfei Hu, Fang-Ming Hung, Feng Liu

llms agents safety

One email a morning, machine-written

Related stories

Subject-Conditioned Glucose Forecasting in Type-1 Diabetes

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

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

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

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

DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening