Patients' unmet information needs and gaps of obstetric ultrasound exam: A qualitative content analysis of social media platforms [0.03%]
患者未满足的产前超声检查信息需求及缺口：一种社交媒体平台的内容分析方法
Eman M Alanazi,Turki M Alanzi,Min Wu et al.
Eman M Alanazi et al.
Purpose: A healthy pregnancy is critical to ensure a healthy birth. Uncertainty and a lack of information often lead to anxiety during pregnancy, which may negatively affect health outcomes. Women become inquisitive durin...
Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins [0.03%]
谁在担心合成数据？医疗数字孪生的混合方法
Joel Vanin,Amit Hagar,James A Glazier
Joel Vanin
Despite rapidly growing volumes of clinical data, precision medicine still faces a structural data deficit: most patients and rare disease variants are sparsely sampled, labels are noisy, and counterfactual outcomes for alternative treatmen...
Excess CMS morbidity and FEMA declarations: Phenome Wide Association Study of Generalized Linear Model interactions between CMS diagnostic code utilization and FEMA Incident types, 1999-2020 [0.03%]
医疗保险管理下的疾病过剩与联邦紧急事务管理局的宣告：1999年至2020年的诊断编码利用情况和联邦紧急事务管理局事件类型之间的广义线性模型相互作用的表型相关性研究
Nick Williams
Nick Williams
Introduction: Despite the availability of descriptive clinical and disaster data, the associations between disaster type and clinical morbidity remains unknown. The Federal Emergency Management Agency (FEMA) responds to d...
State-of-the-art learning COVID-19 vaccine effectiveness using LSTM [0.03%]
利用LSTM学习COVID-19疫苗有效性的最新方法
Chen Shen,Menghan Lin,Yungchun Lee et al.
Chen Shen et al.
The effect of COVID-19 vaccines in reducing hospitalization risks was studied using the Long Short-Term Memory (LSTM) model. We first devised a dynamic environment using an LSTM that characterizes the impact of COVID-19 vaccine administrati...
SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials [0.03%]
基于大型语言模型的肿瘤临床试验安全性和有效性的提取研究
Kyeryoung Lee,Hunki Paek,Liang-Chin Huang et al.
Kyeryoung Lee et al.
Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstra...
WebQuorumChain: A web framework for quorum-based health care model learning [0.03%]
WebQuorumChain：一种基于准联盟的医疗保健模型学习的网页框架
Xiyan Shao,Anh Pham,Tsung-Ting Kuo
Xiyan Shao
Background: Institutions interested in collaborative machine learning to enhance healthcare may be deterred by privacy concerns. Decentralized federated learning is a privacy-preserving and security-robust tool to promote...
The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images [0.03%]
数据增强和迁移学习对三维磁共振图像髋关节分割的深度学习模型性能的影响研究
Eros Montin,Cem M Deniz,Richard Kijowski et al.
Eros Montin et al.
Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation,...
Qualitative stress perfusion American Heart Association plot and outcome prediction using artificial intelligence [0.03%]
基于人工智慧的定性压力灌注美国心脏协会图及预后预测
Ebraham Alskaf,Cian M Scannell,Richard Crawley et al.
Ebraham Alskaf et al.
UMS-Rep: Unified modality-specific representation for efficient medical image analysis [0.03%]
UMS-Rep：高效医学图像分析的统一模式特定表示法
Ghada Zamzmi,Sivaramakrishnan Rajaraman,Sameer Antani
Ghada Zamzmi
Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because...
Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records [0.03%]
使用压力灌注心脏磁共振报告和电子健康记录的自然语言处理进行机器学习结果预测
Ebraham Alskaf,Simon M Frey,Cian M Scannell et al.
Ebraham Alskaf et al.