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).
By Jincheng Yu, Haoyang Li, Yiwen Liu, Shen Liu, Rachel Yuanbao Chen, C. Kent Kwoh, Hongxu Ding, Xiaoxiao Sun
arXiv:2606. 23705v1 Announce Type: cross Abstract: Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating.
By Saba A. Farahani, Elahe Khatibi, Thomas D. Hughes, Ariana M. Nelson, Hung Cao, Amir M. Rahmani
The paper introduces the Relational Hypergraph Transformer (RHT), a unified architecture that models relational databases as hypergraphs and learns pentadimensional embeddings (PentE). RHT applies sparse relational attention whose complexity scales with the average relational degree, making it computationally efficient for large, high‑dimensional, and high‑cardinality datasets. Experiments on the Synthea synthetic electronic health record dataset show that RHT produces more semantically coherent embeddings than tabular, relational, and temporal graph baselines, while remaining scalable, and the authors provide an open‑source implementation and plan clinical validation on MIMIC‑IV.
By Edouard Lansiaux, Hugo Kazzi, Aur\'elien Loison, Slim Hammadi, Emmanuel Chazard
arXiv:2606. 08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon.
By Mahshad Koohi Habibi Dehkordi, Shuxin Zhou, Yehoshua Perl, Fadi P. Deek, James Geller, Gai Elhanan, Andrew J. Einstein, Luke Lindemann, Vipina K. Keloth
arXiv:2608. 02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources.
By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan
arXiv:2608.28608v1 Announce Type: cross
Abstract: Ancient Indian medical texts like Sushruta Samhita have extensive information on diseases, treatments, and surgical techniques. Yet, their ancient fo...
By M. S. Rajeevan, B. Mini Devi, V. S. Anoop, C. Mallikarjuna
arXiv:2410. 15051v3 Announce Type: replace-cross Abstract: Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets.
By Vittorio Torri, Elisa Barbieri, Anna Cantarutti, Carlo Giaquinto, Francesca Ieva
arXiv:2606. 12569v1 Announce Type: cross Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2608.28329v1 Announce Type: cross
Abstract: Medical question answering (QA) systems have become crucial tools for providing reliable health information. But they remain very unexplored for low-...
By Rowzatul Zannat, Abdullah Al Shafi, K. M. Azharul Hasan, Atia Shahnaz Ipa
The paper presents a method for extracting key information from OCR‑digitized clinical reports, addressing challenges posed by heterogeneous documents and noisy OCR output. It introduces an open key space that is iteratively mined, normalized, clustered, and verified to build a canonical key inventory, and defines key coverage as a metric for inventory completeness. Experiments on reports from over 20 hospitals using a 0.2B BERT model show that performance improves steadily with key coverage, achieving high F1 scores when the top 90 keys are covered and outperforming a fine‑tuned Qwen3‑0.6B baseline.
By Yu Wang, Yingyun Li, Ying Qin, Haiyang Qian
arXiv:2604. 25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval of clinical information.
By Faith Wavinya Mutinda, Spandana Makeneni, Anna Lin, Shivaji Dutta, Irit R. Rasooly, Patrick Dibussolo, Shivani Kamath Belman, Hessam Shahriari, Kevin Murphy, Alex B. Ruan, Barbara H. Chaiyachati, Sanjay Chainani, Robert W. Grundmeier, Scott M. Haag, Jeffrey M. Miller, Heather M. Griffis, Ian M. Campbell
arXiv:2609.15713v1 Announce Type: new
Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...
By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li