Machine learning-based models for the prediction of breast cancer recurrence risk [0.03%]
基于机器学习的乳腺癌复发风险预测模型
Duo Zuo,Lexin Yang,Yu Jin et al.
Duo Zuo et al.
Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for ...
Automatic deep learning-based pleural effusion segmentation in lung ultrasound images [0.03%]
基于深度学习的自动肺部超声图像胸腔积液分割技术
Damjan Vukovic,Andrew Wang,Maria Antico et al.
Damjan Vukovic et al.
Background: Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from th...
Abir Boujelben,Ikram Amous
Abir Boujelben
Backgrounds: The size of medical strategies is expected to grow in conjunction with the expansion of modern diseases' complexity. When a strategy includes more than ten statements, its manual management becomes very chall...
A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability [0.03%]
基于混合堆叠集成和核舒布模型的智能胎心监护分类与可解释性方法
Junyuan Feng,Jincheng Liang,Zihan Qiang et al.
Junyuan Feng et al.
Background: Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, ...
Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review [0.03%]
基于电子健康档案数据运用机器学习预测和诊断抑郁症的系统评价研究
David Nickson,Caroline Meyer,Lukasz Walasek et al.
David Nickson et al.
Background: Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used...
Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study [0.03%]
冠脉旁路移植术后急性肾损伤的机器学习预测：真实世界多中心回顾性队列研究
Tianchen Jia,Kai Xu,Yun Bai et al.
Tianchen Jia et al.
Background: Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish predict...
Identifying self-reported health-related problems in home-based rehabilitation of older patients after hip replacement in China: a machine learning study based on Omaha system theory [0.03%]
基于 Omaha 系统理论的中国髋关节置换术后居家康复老人自报的健康相关问题识别研究：一种机器学习方法
Jing Chen,Fan He,Qian Wu et al.
Jing Chen et al.
Background: With the aging of the population, the number of total hip replacement surgeries is increasing globally. Hip replacement has undergone revolutionary advancements in surgical methods and materials. Due to the sh...
Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding [0.03%]
基于字符嵌入的双向长短期记忆神经网络模型：智能分诊提高远程医疗效率和容量
Jinming Shi,Ming Ye,Haotian Chen et al.
Jinming Shi et al.
Background: The widespread adoption of telehealth services necessitates accurate online department selection based on patient medical records, a task requiring significant medical knowledge. Incorrect triage results in co...
Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning [0.03%]
基于机器学习的慢性心力衰竭患者三年全因死亡率的可解释预测模型研究
Chenggong Xu,Hongxia Li,Jianping Yang et al.
Chenggong Xu et al.
Background: The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chroni...
Initial development of tools to identify child abuse and neglect in pediatric primary care [0.03%]
儿科初级保健中识别儿童虐待和疏忽的工具的初步开发
Rochelle F Hanson,Vivienne Zhu,Funlola Are et al.
Rochelle F Hanson et al.
Background: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warrante...