arXiv Machine Learning

Subtyping patients with chronic disease using longitudinal BMI patterns

arXiv:2111. 05385v3 Announce Type: replace Abstract: Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke.

arXiv Machine Learning

Jun 5

A Machine Learning-Based Framework for Discovering Huntington's Disease Stages: Integrating Graph Representation Learning and clustering to Uncover Progression Dynamics in Longitudinal Enroll-HD Dataset

arXiv:2606. 06196v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive brain disorder that gradually affects movement, cognitive function, and behavior.

By Lubna M. Abu Zohair, Marta Vallejo, MD Azher Uddin, John R. Woodward, Hind Zantout

diffusion

arXiv Machine Learning

Jun 10

Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

arXiv:2604. 07085v2 Announce Type: replace Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making.

By Manar D. Samad, Yina Hou, Shrabani Ghosh

rag

arXiv Machine Learning

Jun 30

Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

arXiv:2606. 28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts.

By Md Mozaharul Mottalib, Rahmatollah Beheshti

arXiv Machine Learning

Jun 8

Explaining Unsupervised Disease Staging in Huntington's Disease: Insights into Model Representations and Clusters

arXiv:2606. 07135v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, and behavioral functions, where accurate characterization of disease progression remains essential to improve patient outcome and quality of life.

By Lubna Mahmoud Abu Zohair, Hind Zantout

rag safety

arXiv Machine Learning

Jun 5

Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

arXiv:2606. 05488v1 Announce Type: cross Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data.

By Yue Zhao, Thierry Chekouo, Sandra Safo

arXiv Machine Learning

Aug 3

Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation

arXiv:2607. 28655v1 Announce Type: cross Abstract: Small-area estimation (SAE) enables researchers and policymakers to identify spatial disparities in health outcomes, but survey-based SAE products carry an inherent lag.

By Aanya Gupta, Szandra P\'eter, Sara Von Hoene, Emma Von Hoene, Taylor Anderson

arXiv Machine Learning

Jun 2

Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults

arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.

By Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro

multimodal safety

arXiv AI

Jun 10

Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages

arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.

By Xinze Zhang

arXiv Machine Learning

Jul 17

A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization

arXiv:2607. 14190v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging.

By Zongliang Yue, Qi Li, Terry Heiman-Patterson, Frank Bearoff, Zhaohui Qin, Huanmei Wu

safety data-engineering

arXiv Machine Learning

Jun 16

Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening

arXiv:2606. 16056v1 Announce Type: new Abstract: Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed.

By Black Sun, Chenyi Zhang, Kaiyi Ji, Xi Lu

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 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

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