Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression [0.03%]
青少年、家长和提供者对基于机器学习的选择青年抑郁症治疗方法的临床决策支持系统的态度
Meredith Gunlicks-Stoessel,Yangchenchen Liu,Catherine Parkhill et al.
Meredith Gunlicks-Stoessel et al.
Background: Machine learning based clinical decision support systems (CDSSs) have been proposed as a means of advancing personalized treatment planning for disorders, such as depression, that have a multifaceted etiology,...
Effectiveness of mobile application interventions for stroke survivors: systematic review and meta-analysis [0.03%]
移动应用程序干预对中风幸存者有效性的系统评价和荟萃分析
Wenjing Cao,Azidah Abdul Kadir,Wenzhen Tang et al.
Wenjing Cao et al.
Background: Although smartphone usage is ubiquitous, and a vast amount of mobile applications have been developed for chronic diseases, mobile applications amongst stroke survivors remain unclear. ...
Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies [0.03%]
多准则决策验证基于RBC公式在异质性血红蛋白病中筛查[公式:请参见文本]-地中海贫血的性能
Atul Kumar Jain,Prashant Sharma,Sarkaft Saleh et al.
Atul Kumar Jain et al.
Background: India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cos...
Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach [0.03%]
动脉期增强CT图像的深度学习预测食管鳞状细胞癌浸润深度：二分类方法
Xiaoli Wu,Hao Wu,Shouliang Miao et al.
Xiaoli Wu et al.
Background: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering c...
Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia [0.03%]
用于急性髓系白血病预后生物标志物鉴定的多种机器学习工具
Yujing Cheng,Xin Yang,Ying Wang et al.
Yujing Cheng et al.
Background: Acute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning t...
Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges [0.03%]
用于预测六波疫情期间全国COVID-19患者住院死亡率的回归树模型的开发与评估
M C Schut,D A Dongelmans,D W de Lange et al.
M C Schut et al.
Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction mod...
Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis [0.03%]
腹膜透析患者不良预后预测的机器学习算法
Jie Yang,Jingfang Wan,Lei Feng et al.
Jie Yang et al.
Background: An appropriate prediction model for adverse prognosis before peritoneal dialysis (PD) is lacking. Thus, we retrospectively analysed patients who underwent PD to construct a predictive model for adverse prognos...
A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors [0.03%]
基于熵的人工智能对乳腺肿瘤超声数据的验证研究
Zhibin Huang,Keen Yang,Hongtian Tian et al.
Zhibin Huang et al.
Background: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI m...
Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer [0.03%]
基于粒子滤波的参数估计算法在非小细胞肺癌预后风险评估中的应用进展
Shi Shang,Junyi Yuan,Changqing Pan et al.
Shi Shang et al.
Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recur...
Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study [0.03%]
在虚拟现实环境中理解癌症患者群体以做出更好的临床决策：一项易用性研究
Zhonglin Qu,Quang Vinh Nguyen,Chng Wei Lau et al.
Zhonglin Qu et al.
Background: Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology i...