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:2609.21164v1 Announce Type: new
Abstract: Integrating diverse data modalities --- such as clinical notes, laboratory results, and medical imaging --- is essential for advancing clinical decisio...
By Inyoung Choi, Sukwon Yun, Jiayi Xin, Jie Peng, Tianlong Chen, Qi Long
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
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
Masked language modelling (MLM) remains the dominant pretraining objective for transformer encoders on longitudinal electronic health records (EHRs), but reconstructing individual events can overemphasise noisy observations and fails to encourage learning higher‑level patient states. Joint‑Embedding Predictive Architectures (JEPAs), which predict representations in latent space, were tested on MIMIC‑IV data and, while training stably, consistently underperformed standard MLM. The study found that larger target sizes increase prediction error, yet reducing target size does not improve downstream performance, indicating that stable latent prediction alone is insufficient for competitive EHR representation learning and underscoring the difficulty of defining clinically meaningful latent targets for EHR.
By Ashvin Gupta, Brendan Delaney, Alessandra Russo
The paper "Bridging the Divide: End-to-End Sequence-Graph Learning" introduces BRIDGE, an architecture that jointly learns from sequential and relational data by coupling a sequence model with a graph module under a single objective. It adds a token-level cross-attention layer, TOKENXATTN, to enable fine-grained message passing between events in neighboring sequences. Experiments on relationship prediction and fraud detection show that BRIDGE outperforms static graph models, temporal graph methods, and sequence-only baselines on ranking and classification metrics.
By Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H. Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.
By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi
arXiv:2604.04614v3 Announce Type: replace
Abstract: Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk predic...
By Bohao Li, Tao Zou, Junchen Ye, Yan Gong, Bowen Du
arXiv:2608. 10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness.
By Ruirui Wang, Yanke Li, Manuel G\"unther, Diego Paez-Granados
arXiv:2604. 06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare.
By Yue Fang, Weibin Liao, Yuxin Guo, Jiaran Gao, Hongxin Ding, Jinyang Zhang, Xinke Jiang, Zhibang Yang, Junfeng Zhao, Yasha Wang, Liantao Ma
arXiv:2608.23248v1 Announce Type: cross
Abstract: Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured...
By Siri Willems, James Butterworth, Lore Goetschalckx, Peter Vrancx, Philippe Modard, Elke Giets, Ludovic Denoyer
arXiv:2609.37587v1 Announce Type: cross
Abstract: Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulatio...
By Zijie Meng, Xiwei Dai, Yingying Zhang, Jian Wu, Xian Wu, Zuozhu Liu