Interoperability of heterogeneous health information systems: a systematic literature review [0.03%]
异构卫生信息系统的互操作性: 系统文献回顾
Amir Torab-Miandoab,Taha Samad-Soltani,Ahmadreza Jodati et al.
Amir Torab-Miandoab et al.
Background: The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms amo...
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques [0.03%]
基于MRI的脑肿瘤检测使用卷积深度学习方法和选定的机器学习技术
Soheila Saeedi,Sorayya Rezayi,Hamidreza Keshavarz et al.
Soheila Saeedi et al.
Background: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can ...
Opportunities and challenges of virtual reality-based interventions for patients with breast cancer: a systematic review [0.03%]
基于虚拟现实的乳腺癌患者干预措施的机会和挑战：系统回顾
Alireza Banaye Yazdipour,Soheila Saeedi,Hassan Bostan et al.
Alireza Banaye Yazdipour et al.
Background: Breast cancer is one of the most common cancers diagnosed worldwide and the second leading cause of death among women. Virtual reality (VR) has many opportunities and challenges for breast cancer patients' reh...
Data management system for diabetes clinical trials: a pre-post evaluation study [0.03%]
糖尿病临床试验的数据管理系统：事前事后评估研究
Aynaz Nourani,Haleh Ayatollahi,Masoud Solaymani-Dodaran
Aynaz Nourani
Background: Data management system for diabetes clinical trials is used to support clinical data management processes. The purpose of this study was to evaluate the quality and usability of this system from the users' per...
Automatic medical specialty classification based on patients' description of their symptoms [0.03%]
基于患者症状描述的自动医学专科分类方法研究
Chao Mao,Quanjing Zhu,Rong Chen et al.
Chao Mao et al.
In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not r...
The classification of flash visual evoked potential based on deep learning [0.03%]
基于深度学习的闪光视觉诱发电位分类研究
Na Liang,Chengliang Wang,Shiying Li et al.
Na Liang et al.
Background: Visual electrophysiology is an objective visual function examination widely used in clinical work and medical identification that can objectively evaluate visual function and locate lesions according to wavefo...
Ricardo M S Carvalho,Daniela Oliveira,Catia Pesquita
Ricardo M S Carvalho
Background: Intensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterioration, and a financial burden for healthcare facilities. As healthcare...
Machine learning based efficient prediction of positive cases of waterborne diseases [0.03%]
基于机器学习的有效预测水源性疾病阳性病例的方法
Mushtaq Hussain,Mehmet Akif Cifci,Tayyaba Sehar et al.
Mushtaq Hussain et al.
Background: Water quality has been compromised and endangered by different contaminants due to Pakistan's rapid population development, which has resulted in a dramatic rise in waterborne infections and afflicted many reg...
Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach [0.03%]
使用埃塞俄比亚2016年人口健康调查数据预测埃塞俄比亚育龄妇女的避孕停用：一种机器学习方法
Shimels Derso Kebede,Yakub Sebastian,Abraham Yeneneh et al.
Shimels Derso Kebede et al.
Background: Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces...
Evaluating the success of Iran Electronic Health Record System (SEPAS) based on the DeLone and McLean model: a cross-sectional descriptive study [0.03%]
基于DeLone和McLean模型的伊朗电子健康记录系统(SEPAS)成功度评价：截面描述研究
Azadeh Bashiri,Mohammad Shirdeli,Fatemeh Niknam et al.
Azadeh Bashiri et al.
Background: Quality dimensions are the most important criteria for predicting the success of an information system. The current study aims to evaluate the success of the Iran Electronic Health Record System (SEPAS) based ...