Systematic construction of composite radiation therapy dataset using automated data pipeline for prognosis prediction [0.03%]
基于自动化数据管道的复合放疗数据集的系统构建及其预后预测研究
June Hyuck Lim,Seonhwa Kim,Jun Hyeong Park et al.
June Hyuck Lim et al.
Background: Existing research on medical data has primarily focused on single time-points or single-modality data. This study aims to collect all data generated during radiotherapy comprehensively to improve the treatment...
Analysis of missing data in electronic health records of people with diabetes in primary care in Spain: A population-based cohort study [0.03%]
西班牙初级保健糖尿病患者电子健康记录中缺失数据的分析：基于人群的队列研究
Jose Antonio Quesada,Domingo Orozco-Beltran
Jose Antonio Quesada
Introduction: Researchers conducting studies based on electronic health records (EHRs) often have to deal with missing data. We aimed to analyze patterns of missing data in lipid profile, sociodemographic variables and ri...
A method and validation for auditing e-Health applications based on reusable software security requirements specifications [0.03%]
一种基于可重用软件安全需求说明的电子健康应用程序审计方法及验证
Carlos M Mejía-Granda,José L Fernández-Alemán,Juan M Carrillo de Gea et al.
Carlos M Mejía-Granda et al.
Objective: This article deals with the complex process of obtaining security requirements for e-Health applications. It introduces a tailored audit and validation methodology particularly designed for e-Health application...
Perceptions of healthcare professionals and patients with cardiovascular diseases on mHealth lifestyle apps: A qualitative study [0.03%]
医务人员和心血管疾病患者对mHealth生活方式应用程序的看法：一项定性研究
Sheikh Mohammed Sharifu Islam,Ashal Singh,Sebastiat V Moreno et al.
Sheikh Mohammed Sharifu Islam et al.
Background: Cardiovascular disease (CVD) is the leading cause of death globally and is predominantly associated with a cluster of lifestyle risk factors. Mobile health (mHealth) apps offer the potential to overcome tradit...
Presenting predictions and performance of probabilistic models for clinical decision support in trauma care [0.03%]
创伤护理中临床决策支持的预测和性能的概率模型发布
Cansu Alptekin,Jared M Wohlgemut,Zane B Perkins et al.
Cansu Alptekin et al.
Introduction: Both predictions and performance of clinical predictive models can be presented with various verbal and visual representations. This study aims to investigate how different risk and performance presentations...
Electronic Health Literacy among Older Adults: Development and Psychometric Validation of the Hebrew Version of the Electronic Health Literacy Questionnaire [0.03%]
老年人电子健康素养调查：希伯来版电子健康素养问卷的发展和心理测量学验证
Gizell Green
Gizell Green
Introduction: In the digital age, electronic health literacy (eHealth literacy) has become crucial for maintaining and improving health outcomes. As the population ages, developing and validating tools that accurately mea...
Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission [0.03%]
预测HFpEF患者一年内再住院风险的机器学习模型的开发和验证：短标题：HFpEF再住院的风险预测
Yue Hu,Fanghui Ma,Mengjie Hu et al.
Yue Hu et al.
Background: Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources a...
Data-driven explainable machine learning for personalized risk classification of myasthenic crisis [0.03%]
数据驱动的可解释机器学习在重症肌无力个人风险分类中的应用研究
Sivan Bershan,Andreas Meisel,Philipp Mergenthaler
Sivan Bershan
Objective: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring intensive care treatment and invasive therapies. Classifying patients at high-risk for MC facilitates treatment decisions su...
Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series [0.03%]
基于医疗时间序列的集体和个体异常检测的智能数据驱动医学决策
Farbod Khanizadeh,Alireza Ettefaghian,George Wilson et al.
Farbod Khanizadeh et al.
Background: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interv...
An interpretable machine learning scoring tool for estimating time to recurrence readmissions in stroke patients [0.03%]
一种用于估计卒中患者再入院复发时间的可解释机器学习评分工具
Xiao Luo,Xin Cui,Rui Wang et al.
Xiao Luo et al.
Background: Stroke recurrence readmission poses an additional burden on both patients and healthcare systems. Risk stratification aims to accurately divide patients into groups to provide targeted interventions at reducin...