High-dimensional machine learning models for prediction of heart failure in more than 400 000 men and women from the UK Biobank [0.03%]
基于英国生物银行中40多万男性和女性的心力衰竭预测的高维机器学习模型
Thomas F Kok,Navin Suthahar,Jesse H Krijthe et al.
Thomas F Kok et al.
Aims: We aimed to compare performances of conventional survival models with machine learning (ML) survival models for incident heart failure (HF) in men and women without prevalent HF, cardiomyopathy (CM) or ischaemic hea...
A deep learning-based pipeline for large-scale echocardiography data curation and measurements [0.03%]
基于深度学习的大型心脏超声数据管理和测量流程管道
Jieyu Hu,Sindre Hellum Olaisen,David Pasdeloup et al.
Jieyu Hu et al.
Background: Echocardiographic image data accumulating in echo labs are a highly valuable but underutilized resource for cardiac imaging research. Despite the availability of large image databases, quantitative measurement...
Echocardiographic measures read by artificial intelligence enable accurate and rapid prediction of the worsening of heart failure [0.03%]
人工智能读取的超声心动图指标能够准确快速地预测心力衰竭恶化
Tony Hauptmann,Sven-Oliver Tröbs,Andreas Schulz et al.
Tony Hauptmann et al.
Aims: Automatic echocardiographic measurements using artificial intelligence have shown promising results; however, they have not been compared with manual measurements regarding heart failure (HF) progression and algorit...
A multi-query, multimodal, receiver-augmented solution to extract contemporary cardiology guideline information using large language models [0.03%]
基于大规模语言模型的多查询、跨模态、接收器增强的心脏病学指南信息提取方法
Robert M Radke,Gerhard-Paul Diller,Rohan G Reddy et al.
Robert M Radke et al.
Aims: The aim of the current study was to assess the utility of a state-of-the-art large language model (LLM) based on curated, defined clinical practice recommendations to support clinicians in obtaining point-of-care gu...
Prognostic stratification of familial hypercholesterolaemia patients using AI algorithms: a gender-specific approach [0.03%]
使用AI算法对家族性高胆固醇血症患者进行预后分层的性别差异研究
Alberto Zamora,Luis Masana,Fernando Civeira et al.
Alberto Zamora et al.
Aims: Familial hypercholesterolaemia (FH) is the most prevalent autosomal dominant disorder, affecting about 1 in 200-250 individuals. It is the leading cause of early and aggressive coronary artery disease. ...
AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients [0.03%]
基于人工智能的心电图衍生生物年龄在心血管和急症患者中的死亡率预测作用
Daniel Pavluk,Fabian Theurl,Samuel Proell et al.
Daniel Pavluk et al.
Aims: Artificial Intelligence (AI) models applied to standard 12-lead ECGs enable estimation of biological age (AI-ECG age), which has shown prognostic value in general populations. However, its clinical utility in high-r...
Accessibility and usage patterns of wearable devices among Chinese adults: the Huawei Blood Pressure Health Study [0.03%]
中国成年人可穿戴设备的使用情况和使用模式——华为血压健康研究项目
Ying Wang,Shan-Shan Zhou,Yu-Qi Liu et al.
Ying Wang et al.
Aims: This study aims to investigate the ownership of wearable health devices across different demographic groups and usage patterns among Chinese adults. ...
Cardiac surveillance of childhood cancer using artificial intelligence-enabled electrocardiograms [0.03%]
基于人工智能心电图的儿童癌症心脏监测
Ivor B Asztalos,Amy Li,Victoria L Vetter et al.
Ivor B Asztalos et al.
Aims: To assess the potential for artificial intelligence-enabled electrocardiogram (AI-ECG) to serve as a long-term cardiac surveillance tool and predict left ventricular systolic dysfunction in childhood cancer patients...
Patient and physician perspectives on smartwatch-based out-of-hospital cardiac arrest detection [0.03%]
患者和医师对基于智能手表的院外心脏骤停检测的看法
Marijn Eversdijk,Marieke A R Bak,Lukas R C Dekker et al.
Marijn Eversdijk et al.
Aims: The potential application of wearable technology solutions for detecting out-of-hospital cardiac arrest (OHCA) is increasingly explored to enhance survival outcomes, but questions related to device accuracy, psychol...