Neuro-signaling techniques in video gaming endorsements: a cognitive and neural dynamics approach [0.03%]
基于认知和神经动力学的视频游戏推广中的神经信号技术研究
Kuppan Chetty Ramanathan,Adalarasu Kanagasabai,Arunkumar Pinagapani et al.
Kuppan Chetty Ramanathan et al.
Background and objective: Technological advancements have significantly transformed the gaming industry, shifting from computer to mobile games. According to a global games market report, the gaming market was projected t...
Multi-dimensional perceptual quality assessment for magnetic resonance images [0.03%]
磁共振图像多维感知质量评估方法研究
Quankeng Huang,Yuqi Tang,Hao Li et al.
Quankeng Huang et al.
Objectively and accurately evaluating the quality of Magnetic Resonance Images (MRI) remains a challenging task. Current mainstream approaches for image quality assessment (IQA) are primarily developed for the general domain. However, these...
Timelygpt: extrapolatable transformer pre-training for long-term time-series forecasting in healthcare [0.03%]
基于变压器的预训练方法TimelyGPT：适用于医疗保健长期时间序列预测
Ziyang Song,Qincheng Lu,Hao Xu et al.
Ziyang Song et al.
Purpose: Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing and Computer Vision domains. However, the development of PTMs on healthcare time-seri...
Supervised prediction of post-stroke upper limb motor recovery with uncertain knowledge graph and large language model [0.03%]
基于不确定知识图和大型语言模型的卒中后上肢运动恢复监督预测方法研究
Tianxing Wu,Xixi Wu,Jian Li et al.
Tianxing Wu et al.
Purpose: Accurate prediction of post-stroke upper limb motor recovery is crucial for developing stroke rehabilitation strategies. However, existing methods often focus on static predictions at a fixed post-stroke time poi...
Enhancing LLM-based clinical reasoning in anesthesiology via graph-augmented retrieval and explainable generation [0.03%]
通过图增强检索和可解释生成改进麻醉学中基于LLM的临床推理
Meng Wang,Yangyang Shen,Bingcheng Zhao et al.
Meng Wang et al.
Purpose: This study aims to enhance the capabilities of large language models (LLMs) in anesthesiology decision support, leveraging a graph-based Retrieval-Augmented Generation (RAG) framework to improve analytical reason...
Knowledge-enhanced medical image classification via descriptive priors from large language models [0.03%]
基于大规模语言模型的描述先验知识增强的医学图像分类方法
Yuhang Zhang,Yiming Xu,Peilin Chen et al.
Yuhang Zhang et al.
Medical image classification aims to categorise clinically significant imaging patterns, thereby facilitating accurate and timely diagnosis. However, existing approaches predominantly rely on visual features extracted from raw pixel data, o...
Predictive modeling of depression and anxiety after myocardial infarction: a study on influential factors and heart rate variability [0.03%]
心肌梗死后抑郁和焦虑的预测模型研究及其影响因素和心率变异性分析
Luana Lorena Moreira,Marcelle Sá,Lucas Salgado Rezende de Mendonça et al.
Luana Lorena Moreira et al.
Background: Myocardial infarction (MI) can lead to significant psychological distress, adversely affecting recovery and prognosis. This study aimed to investigate post-MI depression and anxiety, identify key predictors in...
Enhancing medical image report generation using a self-boosting multimodal alignment framework [0.03%]
基于自提升多模态对齐框架的医学影像报告生成方法
Aqib Nazir Mir,Danish Raza Rizvi,Iqra Nissar
Aqib Nazir Mir
Artificial intelligence has revolutionized medical image analysis by enabling automated, accurate, and efficient diagnostic solutions. This study introduces a novel self-boosting multimodal alignment framework for automated medical image re...
Clustering environmental pollutants associated with increased risk of metabolic disease: a hierarchical analysis [0.03%]
分层分析环境污染物与代谢性疾病风险增加相关性的研究
Brooke Scardino,Akshat Agrawal,Diensn G Xing et al.
Brooke Scardino et al.
Background: Metabolic syndrome (MetS), which affects one-third of the population of the United States, is a risk factor for chronic diseases such as cardiovascular diseases, stroke, and type 2 diabetes mellitus. Heavy met...
Introduction of hierarchical deep features based on the VGG-16 model for enhancing diabetic retinopathy classification [0.03%]
基于VGG-16模型的分级深度特征在糖尿病视网膜病变分类中的应用研究
Fatemeh Taheri,Kambiz Rahbar
Fatemeh Taheri
Diabetic Retinopathy (DR) is a common ocular disease that presents a significant risk of vision loss in individuals with diabetes. Accurate DR classification is critical for preventing disease progression and preserving patients' vision. Ho...