In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and procedure codes) is fundamental for downstream clinical prediction. However, ro bust concept representation learning is hindered by two key challenges: (i) clinically important cross-type dependencies (e.g., diagnosis medication and medication-procedure relations) are often missing or incomplete in existing ontology resources, limiting the ability to model complex EHR patterns; and (ii) rich clinical semantics are often missing from structured resources, and even when available as text, are difficult to integrate with KG structure for representation learning. To address these challenges, we present MedCo, an LLM empowered graph learning framework for medical concept representation. MedCo first builds a global knowledge graph (KG) over medical codes by combining statistically reliable associations mined from EHRs with type-constrained LLM prompting to infer semantic relations. It then utilizes LLMs to enrich the KG into a text-attributed graph by generating node descriptions and edge rationales, providing semantic signals for both concepts and their relationships. Finally, MedCo jointly trains a LoRA-tuned LLaMA text encoder with a heterogeneous GNN, fusing text semantics and graph structure into unified concept embeddings. Extensive experiments on MIMIC-III and MIMIC-IV show that MedCo consistently improves prediction performance and serves as an effective plug-in concept encoder for standard EHR pipelines.
Peking University（北京大学）；Shanghai University of Finance and Economics（上海财经大学）
Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared sample…
Electronic medical record (EMR) data contains historical sequences of visits of patients, and each visit contains rich information, such as patient demographics, hospital utilisation and medical codes, including diagnosis, procedure and medication codes. Most existing EMR embedding methods capture visit-code associations by constructing input visit representations as binary vectors with a static vocabulary of medical codes. With this limited representation, they fail in encapsulating rich attribute information of visits (demographics and utilisation information) and/or codes (e.g., medical cod…
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
GE Healthcare（GE医疗）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡尔伊利诺伊医学院）
Representation Learning of Structured Data for Medical Foundation Models
ASUS Intelligent Cloud Services (AICS)（华硕智能云服务（AICS））；Imperial College London（伦敦帝国学院）；University of Manchester（曼彻斯特大学）；Cheng Hsin General Hospital（程新总医院）；Crayon Software（克雷扬软件公司）
Sun Yat-sen University（中山大学）；Galixir Technologies Ltd（Galixir科技有限公司）
Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry and drug designs. Recent researches abstract molecules as attributed graphs and employ graph neural networks (GNN) for molecular representation learning, which have made remarkable achievements in molecular graph modeling. Albeit powerful, current models either are based on local aggregation operations and thus miss higher-order graph properties or focus on only node information without fully using the edge information. For this sake, we propose a Communicative Message P…
Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models
IEM Kolkata（印度工程管理学院 Kolkata校区）；School of UEMK Kolkata（UEMK Kolkata学院）；Heritage Institute of Technology Kolkata（加尔各答遗产技术学院）；Indian Institute of Information Technology, Kalyani（卡利亚尼印度信息技术学院）
National Research University Higher School of Economics（国立研究型大学高等经济学院）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as word embeddings and pre-trained language models. Mutual enrichment of neurolinguistics and language technologies leads to development of brain-aware natural language representations. The importance of this research area is emphasized by medical applications.
IBM Research（IBM研究院）；Ain Shams University（艾因夏姆斯大学）
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size. This can be attributed to the uneven coverage of medical concepts in EHRs. In this paper, we propose OMTL, an Ontology-driven Multi-Task Learning framework, that is designed to overcome such data limitations. The key contribution of our work is the effective use of knowledge from a predefined well-established medical relationship graph (ontology) to construct a novel deep learning network architecture that mirr…
University of Florida（佛罗里达大学）；Herbert Wertheim College of Engineering（赫伯特·韦特海姆工程学院）；McKnight Brain Institute（麦克奈特脑研究所）；College of Public Health and Health Professions（公共卫生与健康职业学院）；College of Medicine（医学院）；United States Air Force Research Laboratory（美国空军研究实验室）
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages …
University of Science and Technology of China（中国科学技术大学）；Huawei Technologies（华为技术有限公司）；The First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）
Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect w…
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
University of Applied Sciences and Arts Dortmund（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）；Institute for AI in Medicine (IKIM)（医学人工智能研究所）；University Medicine Essen（埃森大学医学中心）；FernUniversität in Hagen（哈根函授大学）；NVIDIA（英伟达公司）；College of Medicine, University of Florida（佛罗里达大学医学院）；University of Florida Health Cancer Center（佛罗里达大学健康癌症中心）；University of Marburg（马尔堡大学）；University of Mannheim（曼海姆大学）；TU Dortmund（多特蒙德工业大学）
