Attention-based synthetic data generation for calibration-enhanced survival analysis: A case study for chronic kidney disease using electronic health records [0.03%]
基于注意力机制的合成数据生成在校准增强生存分析中的应用——以电子健康记录中慢性肾病为例的研究
Nicholas I-Hsien Kuo,Blanca Gallego,Louisa Jorm
Nicholas I-Hsien Kuo
Objectives: Access to real-world healthcare data is constrained by privacy regulations and data imbalances, hindering the development of fair and reliable clinical prediction models. Synthetic data offers a potential solu...
LLM-DQR: Large language model-based automated generation of data quality rules for electronic health records [0.03%]
基于大语言模型的电子健康记录数据质量规则自动生成（LLM-DQR）
Shuyang Xie,Hailing Cai,Yaoqin Sun et al.
Shuyang Xie et al.
Objective: To develop and evaluate LLM-DQR, an automated approach using large language models to generate electronic health record data quality rules, addressing the limitations of current manual and automated methods tha...
Clinical pathway-aware large language models for reliable and transparent medical dialogue [0.03%]
基于临床路径的大语言模型在可靠和透明医疗对话中的应用研究
Jiageng Wu,Xian Wu,Yefeng Zheng et al.
Jiageng Wu et al.
Objective: Large language models (LLMs) offer promising potential in answering real-time medical queries, but they often produce lengthy, generic, and even hallucinatory responses. We aim to develop a reliable and interpr...
Multimodal large language models and mechanistic modeling for glucose forecasting in type 1 diabetes patients [0.03%]
多模态大型语言模型及机制建模在1型糖尿病患者血糖预测中的应用
J C Wolber,M E Samadi,J Sellin et al.
J C Wolber et al.
Introduction: Management of type 1 Diabetes remains a significant challenge as blood glucose levels can fluctuate dramatically and are highly individual. We introduce an innovative approach that combines multimodal Large ...
Scalable scientific interest profiling using large language models [0.03%]
基于大规模语言模型的可扩展科学兴趣画像构建方法研究
Yilun Liang,Gongbo Zhang,Edward Sun et al.
Yilun Liang et al.
Objective: Research profiles highlight scientists' research focus, enabling talent discovery and fostering collaborations, but they are often outdated. Automated, scalable methods are urgently needed to keep these profile...
Vision-language model-based semantic-guided imaging biomarker for lung nodule malignancy prediction [0.03%]
基于视觉-语言模型的语义引导型肺结节成像生物标志物用于恶性预测
Luoting Zhuang,Seyed Mohammad Hossein Tabatabaei,Ramin Salehi-Rad et al.
Luoting Zhuang et al.
Objective: Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sen...
TransDiffECG: Semantically controllable ECG synthesis via transformer-based diffusion modeling [0.03%]
基于变压器的扩散模型的语义可控心电图合成方法TRANSDIFFECG
Yuxin Lin,Jing Ma,Wei Wang et al.
Yuxin Lin et al.
Objective: Existing generative models for electrocardiogram (ECG) synthesis often lack fine-grained, interpretable control, limiting their utility for addressing data scarcity and imbalance. This study aims to develop a m...
Exploring multimodal large language models on transthoracic Echocardiogram (TTE) tasks for cardiovascular decision support [0.03%]
多模态大型语言模型在经胸超声心动图(TTE)任务中的探索以支持心血管决策支持
Jianfu Li,Yiming Li,Zenan Sun et al.
Jianfu Li et al.
Objective: Multimodal large language models (LLMs) offer new potential for enhancing cardiovascular decision support, particularly in interpreting echocardiographic data. This study systematically evaluates and benchmarks...
Scaling up biomedical vision-language models: Fine-tuning, instruction tuning, and multi-modal learning [0.03%]
生物医学视觉语言模型的扩展：微调、指令调优和多模态学习
Cheng Peng,Kai Zhang,Mengxian Lyu et al.
Cheng Peng et al.
Objective: To advance biomedical vision language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handling long text, explore strategi...
Enhancing AI-based diabetic retinopathy screening in low- and middle-income countries with synthetic data [0.03%]
利用合成数据增强人工智能在中低收入国家糖尿病视网膜病变筛查中的应用
Zitao Shuai,Chenwei Wu,Zhengxu Tang et al.
Zitao Shuai et al.
Objective: AI-based DR screening is promising in low- and middle-income countries (LMICs), where limited human resources constrain access to specialist-led programs. However, current systems often degrade under real-world...