Kurt Miller,Qiuhao Lu,William Hersh et al.
Kurt Miller et al.
Objectives: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. Howe...
Gelei Xu,Xueyang Li,Yixiong Chen et al.
Gelei Xu et al.
Objective: This survey aims to systematically map the rapidly evolving landscape of AI agents in healthcare. It addresses the critical need to adapt general-purpose agentic frameworks characterized by autonomy, planning, ...
Generative modeling of clinical time series via latent stochastic differential equations [0.03%]
基于潜状态随机微分方程的临床时间序列生成模型
Muhammad Aslanimoghanloo,Ahmed ElGazzar,Marcel van Gerven
Muhammad Aslanimoghanloo
Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant chal...
Towards interpretable AI in personalized medicine through a radiological-biological radiomics dictionary linking semantic Lung-RADS and imaging radiomics features [0.03%]
通过放射学-生物学生物标志物词典实现可解释的人工智能在精准医学中的应用：语义Lung-RADS与影像组学特征的联系
Ali Fathi Jouzdani,Shahram Taeb,Mehdi Maghsudi et al.
Ali Fathi Jouzdani et al.
Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival largely dependent on early detection. Standard-dose computed tomography (CT) screening, guided by the Lung Imaging Rep...
A pipeline towards missing IS-A relationship discovery in the Gene Ontology [0.03%]
一种寻找本体中缺失IS-A关系的途径——以Gene Ontology为例
Dong Xiao,Lingyun Luo,Pingjian Ding et al.
Dong Xiao et al.
As the Gene Ontology (GO) serves as a foundational resource for biological knowledge, ensuring the completeness of its semantic structure is critically important. In this study, we introduce a novel automated pipeline to identify missing IS...
Towards Reliable Clinical Data: A Collaborative Data Governance Architecture with Lifecycle Integration [0.03%]
面向可靠临床数据的生命 cycle集成型协同治理架构
Xiaojun Li,Ling Li,Chuanzi Yang et al.
Xiaojun Li et al.
Objective: Clinical data are critical for healthcare decision-making and research, but persistent concerns over accuracy, completeness, and consistency limit real-world reliability. Existing quality frameworks often empha...
Learning to take it personally: Precision drug repurposing through patient-specific loss on knowledge graphs using Biobank data [0.03%]
基于生物银行数据的知识图谱通过患者特异性损失进行药物再利用的精准学习
Çerağ Oğuztüzün,Zhenxiang Gao,Jing Li et al.
Çerağ Oğuztüzün et al.
Objective: Precision medicine requires drug repurposing methods that adapt to individual patient profiles while working within regulatory frameworks. Existing approaches apply uniform models to all patients, only using in...
AI-assisted protocol information extraction for improved accuracy and efficiency in clinical trial workflows [0.03%]
基于人工智能的协议信息提取在临床试验工作流程中的应用研究以提升准确性和效率
Ramtin Babaeipour,François Charest,Madison Wright
Ramtin Babaeipour
Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial teams. Structuring protocol content into standard formats has the potential to improve efficiency, sup...
A multi-modal survival prediction framework with group-based batch training and structural consistency alignment [0.03%]
基于分组批量训练和结构一致对齐的多模态生存预测框架
Wei Li,Xiangyu Tan
Wei Li
Objective: Integrating whole-slide images (WSIs) with transcriptomic profiles is pivotal for enhancing cancer survival prediction. However, the intrinsic gigapixel resolution and variable sequence lengths of WSIs create a...
Letter to the Editor: Comment on "quantifying the effect of behaviour self-regulation on well-being through causal analysis: A methodological framework for longitudinal health data" [0.03%]
来信：有关“通过因果分析量化行为自律对幸福感的影响：纵向健康数据的方法框架”的评论
Hasan Nawaz Tahir,Mohammad Idrees,Umema Tariq et al.
Hasan Nawaz Tahir et al.