A WeChat applet-based national remote emergency system for malignant hyperthermia in China: a usability study [0.03%]
基于微信小程序的中国恶性高热远程应急系统使用性研究
Hong Yu,Lingcan Tan,Tao Zhu et al.
Hong Yu et al.
Background: Malignant hyperthermia (MH) is a rare anesthetic emergency with a high mortality rate in China. We developed a WeChat applet-based National Remote Emergency System for Malignant Hyperthermia (MH-NRES) to provi...
Barriers and facilitators of using health information technologies by women: a scoping review [0.03%]
女性使用健康信息技术的障碍与促进因素:系统综述
Khadijeh Moulaei,Reza Moulaei,Kambiz Bahaadinbeigy
Khadijeh Moulaei
Background and aim: Health information technologies play a vital role in addressing diverse health needs among women, offering a wide array of services tailored to their specific requirements. Despite the potential benefi...
Utilization of patient portals: a cross-sectional study investigating associations with mobile app quality [0.03%]
关于移动应用程序质量与患者门户使用情况的关联性调查——横断面研究
Noha El Yaman,Jad Zeitoun,Rawan Diab et al.
Noha El Yaman et al.
Background: Mobile apps facilitate patients' access to portals and interaction with their healthcare providers. The COVID-19 pandemic accelerated this trend globally, but little evidence exists on patient portal usage in ...
Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study [0.03%]
基于超声图像的非肿块型乳腺病变的智能检测与分类:一种对比研究方法
Guoqiu Li,Hongtian Tian,Huaiyu Wu et al.
Guoqiu Li et al.
Background: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. ...
Application of artificial neural network in daily prediction of bleeding in ICU patients treated with anti-thrombotic therapy [0.03%]
人工神经网络在抗凝治疗ICU患者每日出血风险预测中的应用研究
Daonan Chen,Rui Wang,Yihan Jiang et al.
Daonan Chen et al.
Objectives: Anti-thrombotic therapy is the basis of thrombosis prevention and treatment. Bleeding is the main adverse event of anti-thrombosis. Existing laboratory indicators cannot accurately reflect the real-time coagul...
The effect of capacity building evidence-based medicine training on its implementation among healthcare professionals in Southwest Ethiopia: a controlled quasi-experimental outcome evaluation [0.03%]
埃塞俄比亚西南部卫生专业人员能力建设循证医学培训对其实施的影响:一项控制准实验结局评价
Habtamu Setegn Ngusie,Mohammadjud Hasen Ahmed,Shegaw Anagaw Mengiste et al.
Habtamu Setegn Ngusie et al.
Background: Evidence-based medicine (EBM) bridges research and clinical practice to enhance medical knowledge and improve patient care. However, clinical decisions in many African countries don't base on the best availabl...
Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study [0.03%]
基于机器学习的慢性肾脏病尿蛋白进展的风险因素挖掘与预测研究
Yufei Lu,Yichun Ning,Yang Li et al.
Yufei Lu et al.
Background: Chronic kidney disease (CKD) is a global public health concern. Therefore, to provide timely intervention for non-hospitalized high-risk patients and rationally allocate limited clinical resources is important...
Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data [0.03%]
利用高维电子健康记录更新重症监护病房中的死亡风险估计(存在缺失数据的情况下)
Bertrand Bouvarel,Fabrice Carrat,Nathanael Lapidus
Bertrand Bouvarel
Background: The risk of mortality in intensive care units (ICUs) is currently addressed by the implementation of scores using admission data. Their performances are satisfactory when complications occur early after admiss...
Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment [0.03%]
预测新冠肺炎康复患者肺弥散功能障碍的解释性机器学习模型
Fu-Qiang Ma,Cong He,Hao-Ran Yang et al.
Fu-Qiang Ma et al.
Introduction: The COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors' prog...
Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study [0.03%]
基于临床可用变量的痴呆症预测:一项使用机器学习的概念验证研究。AGES-雷克雅未克研究
Emma L Twait,Constanza L Andaur Navarro,Vilmunur Gudnason et al.
Emma L Twait et al.
Background: Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need ...