HERMES is a graph-based framework that uses only clinical text to predict patient outcomes. It builds personalized Knowledge Graphs from clinical notes via Large‑Language‑Model‑guided extraction and Contrastive Logic Modeling, capturing temporal dynamics and treatment changes. A Graph Attention Network then synthesizes patient representations, and experiments on MIMIC‑III and MIMIC‑IV show HERMES outperforms text‑only baselines for in‑hospital mortality and 30‑day readmission prediction.
By Gia-Bach Nguyen, Hoang-Ha Nguyen, Tuan-Cuong Vuong, Trang Mai Xuan, Duy Quoc Ngo, Tien-Cuong Nguyen, Huan Vu, Thien Van Luong
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
arXiv:2609.22239v1 Announce Type: new
Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...
By Jakir Hossain, Yi-Fei Zhao, Hongjian Wang, Minmei Shih, Katie Leigh Mullen, Ahmad P. Tafti, Leming Zhou, Manoj Purohit, William Hogan, Jay Zeng, Elizabeth Skidmore, Yanshan Wang
arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.
By Thummaluru Siddartha Reddy, Vempalli Naga Sai Saketh, Yash Punjabi, Mahesh Chandran
EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
By Chang Zong, Jinyu Chen, Sicheng Lv, Si-tu Xue, Huilin Zheng, Jian Wan, Lei Zhang
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.
By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim
arXiv:2510.03536v3 Announce Type: replace-cross
Abstract: Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across mult...
By Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Hao Xu, Ke Yuan, Iadh Ounis
arXiv:2608.15382v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy r...
By Ummara Mumtaz, Aimen Noor, Awais Ahmed
arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.
By Ummara Mumtaz, Aimen Noor, Awais Ahmed
arXiv:2606. 29876v1 Announce Type: cross Abstract: Modern large language models (LLMs) reach 60-70% diagnostic accuracy on complex clinical case benchmarks, but accuracy alone cannot distinguish stable clinically-grounded reasoning from pattern matching.
By Nisarg A. Patel (University of California, San Francisco)
The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
By Anirudh Rayas, Yuan Wang, Pavan Turaga