An early detection and segmentation of Brain Tumor using Deep Neural Network [0.03%]
基于深度神经网络的脑肿瘤早期检测与分割
Mukul Aggarwal,Amod Kumar Tiwari,M Partha Sarathi et al.
Mukul Aggarwal et al.
Background: Magnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care pl...
Dongmei Tang,Haiyan Wang,Dantong Gu et al.
Dongmei Tang et al.
Objective: Tinnitus is a highly prevalent hearing disorder, and the burden of tinnitus diagnosis and treatment is very heavy, especially in China. In order to better benefit the majority of tinnitus patients, we developed...
Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022 [0.03%]
基于机器学习预测接受抗逆转录病毒治疗的艾滋病患者的病毒学失败:2022年埃塞俄比亚安博拉地区贡达尔大学综合和专科医院的研究
Daniel Niguse Mamo,Tesfahun Melese Yilma,Makida Fekadie et al.
Daniel Niguse Mamo et al.
Background: Treatment with effective antiretroviral therapy (ART) reduces viral load as well as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded availability of antiretroviral therapy aro...
The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model [0.03%]
基于可解释机器学习模型的男性乳腺癌患者远处转移风险预测
Xuhai Zhao,Cong Jiang
Xuhai Zhao
Objectives: This research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model...
The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions [0.03%]
人工智能对以患者为中心的医患关系的影响:一些问题与解决方案
Aurelia Sauerbrei,Angeliki Kerasidou,Federica Lucivero et al.
Aurelia Sauerbrei et al.
Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given ...
Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis [0.03%]
颅内动脉瘤夹闭术中输血风险预测列线图的构建和验证:回顾性分析
Shugen Xiao,Fan Liu,Liyuan Yu et al.
Shugen Xiao et al.
Purpose: Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surge...
Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region [0.03%]
基于机器学习和统计技术的心血管疾病发病预测:来自东地中海地区16年的队列研究
Kamran Mehrabani-Zeinabad,Awat Feizi,Masoumeh Sadeghi et al.
Kamran Mehrabani-Zeinabad et al.
Background: Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machin...
Machine learning prediction of mortality in Acute Myocardial Infarction [0.03%]
急性心肌梗死患者死亡率的机器学习预测模型
Mariana Oliveira,Joana Seringa,Fausto José Pinto et al.
Mariana Oliveira et al.
Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients wi...
A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods [0.03%]
基于Bootstrap的模拟方法估计诊断延误频率、持续时间和风险因素的全面框架
Aaron C Miller,Joseph E Cavanaugh,Alan T Arakkal et al.
Aaron C Miller et al.
Background: The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different d...
Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning [0.03%]
基于生物信息学分析和机器学习的支气管肺发育不全疾病的铜死亡相关基因及特征标志物的识别与验证
Mingxuan Jia,Jieyi Li,Jingying Zhang et al.
Mingxuan Jia et al.
Background: Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine ...