Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study [0.03%]
一项回顾性研究：用于预测发热婴幼儿侵袭性细菌感染的可解释深度学习模型
Ying Yang,Yi-Min Wang,Chun-Hung Richard Lin et al.
Ying Yang et al.
Background: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. Howe...
Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review [0.03%]
临床药学服务的人工智能应用程序和工具的发展：系统评价研究
Florence Ranchon,Sébastien Chanoine,Sophie Lambert-Lacroix et al.
Florence Ranchon et al.
Objective: Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analy...
AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments [0.03%]
基于深度学习的自由生活环境下穿戴加速度传感器的全天活动行为分类框架_accnet24
Vahid Farrahi,Usman Muhammad,Mehrdad Rostami et al.
Vahid Farrahi et al.
Objective: Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a c...
An intelligent model involving multi-channels spectrum patterns based features for automatic sleep stage classification [0.03%]
一种基于多通道频谱模式特征的智能模型用于自动睡眠分期分类
Shahab Abdulla,Mohammed Diykh,Siuly Siuly et al.
Shahab Abdulla et al.
Effective sleep monitoring from electroencephalogram (EEG) signals is meaningful for the diagnosis of sleep disorders, such as sleep Apnea, Insomnia, Snoring, Sleep Hypoventilation, and restless legs syndrome. Hence, developing an automatic...
Healthcare professionals' digital health competence and its core factors; development and psychometric testing of two instruments [0.03%]
healthcare professionals的数字健康能力及其核心因素；两种工具的心理测量测试和开发
E Jarva,A Oikarinen,J Andersson et al.
E Jarva et al.
Background: Healthcare professionals' digital health competence is an important phenomenon to study as healthcare practices are changing globally. Recent research aimed to define this complex phenomenon and identify the c...
Putting undergraduate medical students in AI-CDSS designers' shoes: An innovative teaching method to develop digital health critical thinking [0.03%]
让医学生本科生担任AI-CDSS设计师：一种培养数字健康批判性思维的创新教学方法
Rosy Tsopra,Nathan Peiffer-Smadja,Caroline Charlier et al.
Rosy Tsopra et al.
Introduction: Digital health programs are urgently needed to accelerate the adoption of Artificial Intelligence and Clinical Decision Support Systems (AI-CDSS) in clinical settings. However, such programs are still lackin...
Digital contact tracing during the COVID-19 pandemic in France: Associated factors and reasons for non-use [0.03%]
法国在COVID-19大流行期间的数字接触者追踪：相关因素和未使用的原因
Rajae Touzani,Emilien Schultz,Stéphanie Vandentorren et al.
Rajae Touzani et al.
Objectives: To estimate the proportion of users of the TousAntiCovid app(lication) and identify factors associated with its non-use for contact tracing. M...
Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment [0.03%]
基于自然语言处理的入组预筛查工具：迭代可用性评估及临床研究工作人员感知
Betina Idnay,Yilu Fang,Caitlin Dreisbach et al.
Betina Idnay et al.
Background: Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research staff manu...
Self-management in heart failure using mHealth: A content validation [0.03%]
使用移动健康进行心力衰竭的自我管理：内容效度验证研究
Martina Fernández-Gutiérrez,Pilar Bas-Sarmiento,Antonio Jesús Marín-Paz et al.
Martina Fernández-Gutiérrez et al.
Aim: To describe the development of a mobile health application -mICardiApp- designed by a multidisciplinary professional team and patients with heart failure and to evaluate its content validity. ...
A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation [0.03%]
基于注意力机制的深度学习方法在膀胱肿瘤分割中的对比研究
Qi Zhang,Yinglu Liang,Yi Zhang et al.
Qi Zhang et al.
Background: Artificial intelligence aided tumor segmentation has been applied in various medical scenarios and showed effectiveness in helping physicians observe the potential malignant tissues. However, little research h...