arXiv AI By Jincheng Yu, Haoyang Li, Yiwen Liu, Shen Liu, Rachel Yuanbao Chen, C. Kent Kwoh, Hongxu Ding, Xiaoxiao Sun

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

Read the original on arXiv AI →

arXiv:2606. 05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
1d ago

PLeDO: Pain Level Detection for Osteoarthritis from EMR Data

arXiv:2608. 15719v1 Announce Type: new Abstract: Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not able to normally repair themselves.

By Yuhao Chen, Jiahao Cai, Nafiz Sadman, Farhana Zulkernine, John Queenan, David Barber
arXiv Machine Learning
Jul 21

Differentiable latent structure discovery for interpretable forecasting in clinical time series

arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.

By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
arXiv AI
Jun 17

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.

By Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti