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.
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.
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.
arXiv:2608. 04193v1 Announce Type: cross Abstract: Language models (LMs) offer strong textual representations for electronic health records (EHRs), but they encode patient sequences in isolation and provide limited explainability.
The paper introduces Clinical Graph-JEPA, a framework for building and refining predictive patient-state knowledge graphs from clinical records. It combines multi-agent relation proposal, ontology-aware normalization, deterministic evidence scoring, and JEPA-based latent refinement to construct evidence-scored graphs from MIMIC-IV data and recover missing clinical relations. Experiments show that injecting discharge-note representations into note-grounded entities boosts leave-one-out MRR by 31% relative improvement.
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).
arXiv:2604. 06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare.
The paper introduces ReTA, a reinforcement‑learning framework that dynamically decides how to augment electronic health record (EHR) graphs with external knowledge graphs (KGs) on a per‑visit basis. ReTA offers three actions—Soft Import, Hard Import, and Skip—allowing it to enrich node features, graft compact KG subgraphs, or leave the graph unchanged depending on the patient’s evolving state and a budget constraint. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that ReTA consistently outperforms strong baselines, transfers across datasets and KGs, and provides interpretable augmentation patterns while maintaining efficiency.
arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.
arXiv:2606. 15155v1 Announce Type: new Abstract: Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare.
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...
The paper proposes a reinforcement learning (RL) fine‑tuning framework for electronic health record (EHR) foundation models, treating them as generative policies over patient trajectories. By framing clinical prediction tasks such as hospital readmission as event‑conditioned, time‑windowed reasoning problems and designing time‑aware, rollout‑sensitive rewards, the authors show that RL fine‑tuning consistently outperforms pre‑trained backbones and strong baselines. The approach enables smaller models to surpass larger pre‑trained models in data‑limited settings, induces positive transfer across tasks, and produces trajectories with stronger structural and semantic alignment to ground truth, improving downstream utility.
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.