A comprehensive evaluation framework for synthetic medical tabular data generation [0.03%]
合成医学表格数据生成的全面评估框架
Anastasia Kurakova,Hajar Homayouni
Anastasia Kurakova
Machine learning (ML) applications have enabled significant advancements in healthcare, such as predicting pandemics, personalizing treatments, and developing life-saving drugs. However, ML model training requires large datasets, which are ...
Xinyao Liu,Junchang Xin,Qi Shen et al.
Xinyao Liu et al.
Objective: Radiology report provides important references for physicians' treatment decisions by including descriptions and diagnostic results of imaging. Automatic generation of radiology report reduces the workload of p...
A LangChain-based pipeline for one-shot synthetic text generation using generative pre-trained transformers in palliative care research [0.03%]
基于LangChain的一次性合成文本生成管道在姑息护理研究中的应用：使用生成式预训练变压器
Isabel Ronan,Patrice Crowley,Eva Rombouts et al.
Isabel Ronan et al.
Objective: As the world's population ages, nursing homes are of increasing importance. In order to care for a growing number of older adults, intelligent technologies are needed. Artificial Intelligence can be utilised to...
From image to report: automating lung cancer screening interpretation and reporting with vision-language models [0.03%]
从图像到报告：使用视觉语言模型自动化肺癌筛查的解读和报告生成
Tien-Yu Chang,Qinglin Gou,Leyi Zhao et al.
Tien-Yu Chang et al.
Objective: Lung cancer is the most prevalent cancer and the leading cause of cancer-related death in the United States. Lung cancer screening with low-dose computed tomography (LDCT) helps identify lung cancer at an early...
MF-DTA: Predicting drug-target affinity with multi-modal feature fusion model [0.03%]
基于多模态特征融合的药物-靶点亲和力预测模型(MF-DTA)
Yanlei Kang,Haoyu Zhuang,Yunliang Jiang et al.
Yanlei Kang et al.
The prediction of drug-target interactions (DTIs) and binding affinities (DTAs) plays a pivotal role in drug discovery and design. However, most existing methods fail to fully exploit the rich multimodal information inherent in molecular st...
Multi-feature machine learning for enhanced drug-drug interaction prediction [0.03%]
基于多特征机器学习的药物相互作用预测方法研究增强
Qiuyang Feng,Xiao Huang
Qiuyang Feng
Drug-drug interactions are a major concern in healthcare, as concurrent drug use can cause severe adverse effects. Existing machine learning methods often neglect data imbalance and DDI directionality, limiting clinical reliability. To over...
A REDCap advanced randomization module to Meet the needs of modern trials [0.03%]
REDCap试验随机模块的高级功能满足现代临床试验的需求
Luke Stevens,Nan Kennedy,Rob J Taylor et al.
Luke Stevens et al.
Objective: Since 2012, the electronic data capture platform REDCap has included an embedded randomization module allowing a single randomization per study record with the ability to stratify by variables such as study sit...
Accelerating probabilistic privacy-preserving medical record linkage: A three-party MPC approach [0.03%]
一种三方安全计算方法以加速概率隐私保护医疗记录关联技术
Şeyma Selcan Mağara,Noah Dietrich,Ali Burak Ünal et al.
Şeyma Selcan Mağara et al.
Objective: Record linkage is essential for integrating data from multiple sources with diverse applications in real-world healthcare and research. Probabilistic Privacy-Preserving Record Linkage (PPRL) enables this integr...
GraphFusion: Integrative prediction of drug synergy using multi-scale graph representations and cell line contexts [0.03%]
基于多尺度图表示和细胞系背景的药物协同作用预测模型GRAPHFUSSION
Biyang Zeng,Shikui Tu,Lei Xu
Biyang Zeng
Predicting the synergy of drug combinations is crucial for cancer treatment and drug development. Accurate prediction requires the integration of multiple types of data, including molecular structures of individual drugs, available synergy ...
Radovan Tomasik,Simon Konar,Niina Eklund et al.
Radovan Tomasik et al.
Objective: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare sys...