CoLa-ICD is a knowledge‑enhanced framework designed to improve automatic medical coding of ICD codes in long, imbalanced clinical documents. It enriches ICD labels with external terms, models dependencies among related codes, and strengthens the alignment between label semantics and clinical evidence, particularly for rare codes. Experiments demonstrate that CoLa-ICD achieves state‑of‑the‑art performance in AUC, F1, and P@k, with larger gains in larger and sparser label spaces.
By Yihang Cheng, Veronica Liesaputra, Andrew Trotman
Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and diverse terms. These challenges are especially sever...
arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.
By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn
arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.
By Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh, Shruthi Kashinath Hiremath, Katrin H\"ansel, Thomas Pl\"otz
arXiv:2608.20887v1 Announce Type: cross
Abstract: Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for m...
By Xubin Chen, Yipeng Zhou, Wen Sun, Chengkai Huang, Xiaoming Fu, Quan Z. Sheng
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
By Jiahui Li, Ruili Fang, Zishuai Liu, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou
REFINE is a framework that refines medical concept representations by creating patient‑specific temporal graphs from a global text‑attributed knowledge graph. It uses a reinforcement learning policy to allocate a personalized graph expansion budget for each observed code, then processes the resulting graph with a heterogeneous GNN and a frozen LLM that refines representations via graph‑aware soft prompts. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that REFINE consistently improves various EHR prediction backbones, surpasses strong baselines, and shows robust gains across ablation studies, KG selection, and data insufficiency scenarios.
By Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang, Zijun Yao
The paper introduces DeToxR, a reinforcement‑learning‑enhanced large language model designed to support decision making in acute toxicology cases. It fuses unstructured narratives from paramedics and patients with structured vital‑sign data to predict co‑ingested substances across 14 classes. In preliminary validation, DeToxR outperforms baseline models, achieving higher micro‑F1 and recall scores for poison identification.
By Nico Oberl\"ander, David Bani-Harouni, Tobias Zellner, Nassir Navab, Florian Eyer, Matthias Keicher
The paper introduces ICD-Deepresearch, a workflow that combines foundation models for electronic health records (EHR) and language models with medical search and ICD dictionaries to forecast future ICD codes for upcoming clinical encounters. It evaluates candidate code transitions by linking patient evidence, external clinical relations, and exact code semantics within a fixed top‑K budget, using SparseEHR for initial priors, GPT‑5 for complementary forecasts, and a final selection step that validates, deduplicates, and ranks candidates. The method achieves patient‑averaged precision/recall of 24.60/35.09% on MIMIC‑III and 25.14/48.32% on MIMIC‑IV, with physicians rating 51–68% of its retrieved documents as useful, outperforming standalone GPT‑5 web search and Medical Deep Research.
By Junda Wang, Meysam Ghaffari, Akshat Choube, Mohsen Sharifi Renani, Hong Yu, Carlos Morato
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra
arXiv:2606. 01617v1 Announce Type: cross Abstract: Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are costly.
By Tianyi Xu, Yaolun Zhang, Xuan Ouyang, Huazheng Wang
arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.
By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska