Yehia Ibrahim Alzoubi,Alok Mishra
Yehia Ibrahim Alzoubi
As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High ene...
Centralized pooling and federated learning for Canadian patient-level data sharing in multicenter medical AI: A scoping review [0.03%]
加拿大患者级数据在多中心医学AI中的集中式池化和联邦学习：一项范围审查
Omid Jafarinezhad,Qian Zhang,Ryan Rezai et al.
Omid Jafarinezhad et al.
Algorithms that support screening, triage, and treatment decisions depend on training data drawn from patient populations. Limited access to patient-level records across institutions and jurisdictions can reduce representation and contribut...
Patient preference analysis for online consultation based on user-generated content [0.03%]
基于用户生成内容的在线咨询患者偏好分析
Huchang Liao,Yuan Zheng,Xingli Wu et al.
Huchang Liao et al.
Tailoring doctor recommendations to patients' needs and preferences is the core of online consultation platforms. Existing studies encountered challenges in capturing patients' personalized preferences towards different attributes of doctor...
Topo-UNet: A topology-aware multi-task network for pulmonary vessel segmentation [0.03%]
一种拓扑感知的多任务网络用于肺血管分割
Lu Liu,Ye Yuan,Yanxin Ma et al.
Lu Liu et al.
The precise segmentation of pulmonary vessels is crucial for the early diagnosis and treatment of pulmonary diseases. However, vessel images are frequently compromised by high levels of noise and blurred boundaries, which complicate the ext...
Adaptive time-frequency decomposition informer for pathological rest tremor sequence prediction [0.03%]
自适应时频分解告知者用于病理性静息震颤序列预测
Feiyun Xiao,Ruixue Gao,Cheng Huang et al.
Feiyun Xiao et al.
Pathological tremor is a common symptom of various neurological disorders. Pathological tremor signal prediction is important to the tremor suppression equipment. However, how to improve the accuracy of multi-step prediction of tremor motio...
Tackling small sample survival analysis via transfer learning: A study of colorectal cancer prognosis [0.03%]
基于迁移学习的小样本生存分析研究：结直肠癌预后预测研究
Yonghao Zhao,Changtao Li,Chi Shu et al.
Yonghao Zhao et al.
Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals w...
Application research of dynamic chaotic sequence generation mechanism in pre-hospital emergency data encryption [0.03%]
基于动态混沌序列生成机制的院前急救数据加密应用研究
Wei Han,Lu Lu,Jingtao Ma et al.
Wei Han et al.
Background and objectives: In the context of pre-hospital emergency care, the security of patients' physiological data has become increasingly important due to the widespread use of portable and wearable devices. This stu...
DMVHP-IBS: Dynamic feature-integrated multi-modal prediction of virus-host protein interactions and the binding sites [0.03%]
DMVHP-IBS：动态特征融合的病毒宿主互作蛋白及其作用位点预测模型
Lingtao Su,Shiwei Zhao,Gonglei Zhang et al.
Lingtao Su et al.
Accurately predicting virus-host protein interactions(VH-PPI) and their binding sites is essential for understanding viral pathogenic mechanisms and developing drugs and vaccines. Existing sequence- or structure-based approaches still have ...
Multi-domain based heterogeneous network for drug-target interaction prediction [0.03%]
基于多域的异构网络的药物-靶点相互作用预测方法
Changjian Zhou,Yutong Liu,Lu Yu et al.
Changjian Zhou et al.
Recent studies have emphasized the importance of computational approaches in predicting drug-target interactions (DTIs) for drug discovery and chemogenomics studies. Precise prediction of DTIs plays a crucial role in exploring a vast space ...
Measuring the quality of AI-generated clinical notes: A systematic review and experimental benchmark of evaluation methods [0.03%]
人工智能临床记录生成质量评估方法的系统评价与实验基准研究
Alexandra Dahlberg,Tiila Käenniemi,Tiia Winther-Jensen et al.
Alexandra Dahlberg et al.
High-quality clinical documentation is essential for safe and effective care, yet its production remains time consuming and prone to error. Large language models (LLMs) have shown potential for supporting clinical note generation, but their...