Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper [0.03%]
全球共享可靠信息：基于人工智能的医疗保健策略需要外部验证。立场文件
F Pennestrì,F Cabitza,N Picerno et al.
F Pennestrì et al.
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using ...
Safety and accuracy of digitally supported primary and secondary urgent care telephone triage in England: an observational study using routine data [0.03%]
基于常规数据的英格兰数字辅助一级和二级即时电话诊疗的安全性和准确性：观察性研究
Vanashree Sexton,Catherine Grimley,Jeremy Dale et al.
Vanashree Sexton et al.
Background: England's urgent care telephone triage system comprises non-clinician-led primary triage (NHS111) assessment followed, for approximately 50% patients, by clinician-led secondary triage. Digital decision suppor...
Observational Study
BMC medical informatics and decision making. 2025 Feb 3;25(1):52. DOI:10.1186/s12911-025-02888-x 2025
Accounting for racial bias and social determinants of health in a model of hypertension control [0.03%]
考虑种族偏见和健康社会决定因素的高血压控制模型分析
Yang Hu,Nicholas Cordella,Rebecca G Mishuris et al.
Yang Hu et al.
Background: Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of sociodemographic factors. This study aims to identify patients ...
Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning [0.03%]
利用机器学习预测心血管疾病风险并重点关注气流阻塞之间的联系
Xiyu Cao,Jianli Ma,Xiaoyi He et al.
Xiyu Cao et al.
Background: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and int...
Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models [0.03%]
开发预测肾移植后移植物存活的临床预后模型：统计与机器学习模型的比较
Getahun Mulugeta,Temesgen Zewotir,Awoke Seyoum Tegegne et al.
Getahun Mulugeta et al.
Introduction: Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. Th...
A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR [0.03%]
基于电子健康记录的机器学习模型预测阵发性和持续性心房颤动
Yuqi Zhang,Sijin Li,Peibiao Mai et al.
Yuqi Zhang et al.
Background: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a ...
Ali Atshan Abdulredah,Mohammed A Fadhel,Laith Alzubaidi et al.
Ali Atshan Abdulredah et al.
This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple path...
Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data [0.03%]
基于可穿戴设备的肺部术后并发症风险评估研究
Lixuan Li,Yuekong Hu,Zhicheng Yang et al.
Lixuan Li et al.
Background: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning ...
Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling [0.03%]
基于集成机器学习模型优化1型糖尿病低血糖预测
Daphne N Katsarou,Eleni I Georga,Maria A Christou et al.
Daphne N Katsarou et al.
Background: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucos...
Observational Study
BMC medical informatics and decision making. 2025 Jan 31;25(1):46. DOI:10.1186/s12911-025-02867-2 2025
Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches [0.03%]
应用稳健模型检测体质因素与T2DM关联：机器学习方法
Nafiseh Hosseini,Hamid Tanzadehpanah,Amin Mansoori et al.
Nafiseh Hosseini et al.
Background: The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM). ...