Developing and testing the usability, acceptability, and future implementation of the Whole Day Matters Tool and User Guide for primary care providers using think-aloud, near-live, and interview procedures [0.03%]
“全程重要”工具及用户指南的开发和测试：使用思维发声、现场模拟和访谈法评估初级保健提供者的可用性、可接受性和未来实施情况
Tamara L Morgan,Jensen Pletch,Emma Faught et al.
Tamara L Morgan et al.
Background: Canada's 24-Hour Movement Guidelines for Adults have shifted the focus from considering movement behaviours (i.e., physical activity, sedentary behaviour, and sleep) separately to a 24-h paradigm, which consid...
Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning [0.03%]
基于堆叠集成学习的脑血管疾病日住院预测及可解释性研究
Xiaoya Lu,Hang Qiu
Xiaoya Lu
Background: With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing...
Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism [0.03%]
基于分层注意机制的属性异构信息网络在健康保险欺诈检测中的应用研究
Jiangtao Lu,Kaibiao Lin,Ruicong Chen et al.
Jiangtao Lu et al.
Background: With the rapid growth of healthcare services, health insurance fraud detection has become an important measure to ensure efficient use of public funds. Traditional fraud detection methods have tended to focus ...
Accurate breast cancer diagnosis using a stable feature ranking algorithm [0.03%]
基于稳定特征排序算法的乳腺癌精准诊断方法研究
Shaode Yu,Mingxue Jin,Tianhang Wen et al.
Shaode Yu et al.
Background: Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging. ...
Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study [0.03%]
基于机器学习算法的特征选择技术在胃癌患者五年生存预后因素中的优化作用及比较研究
Mohammad Reza Afrash,Esmat Mirbagheri,Mehrnaz Mashoufi et al.
Mohammad Reza Afrash et al.
Background: Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the bes...
Development and user testing of a patient decision aid for cancer patients considering treatment for anxiety or depression [0.03%]
癌症患者治疗焦虑或抑郁的决策辅助工具的研发与用户测试
Rebecca Rayner,Joanne Shaw,Caroline Hunt
Rebecca Rayner
Background: Despite high rates of mental health disorders among cancer patients, uptake of referral to psycho-oncology services remains low. This study aims to develop and seek clinician and patient feedback on a patient ...
Development of an integrated and comprehensive clinical trial process management system [0.03%]
临床试验过程管理系统的设计与开发
Liang Shen,You Zhai,AXiang Pan et al.
Liang Shen et al.
Background: The process of initiating and completing clinical drug trials in hospital settings is highly complex, with numerous institutional, technical, and record-keeping barriers. In this study, we independently develo...
Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study [0.03%]
使用机器学习预测疑似心血管急诊患者的不适当院前转送：回顾性观察研究
Ji Hoon Kim,Bomgyeol Kim,Min Joung Kim et al.
Ji Hoon Kim et al.
Background: This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an...
Observational Study
BMC medical informatics and decision making. 2023 Apr 6;23(1):56. DOI:10.1186/s12911-023-02149-9 2023
Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System [0.03%]
基于美国国家中毒数据系统的决策树与常见机器学习模型预测双胍类和磺脲类中毒的比较分析研究
Omid Mehrpour,Farhad Saeedi,Samaneh Nakhaee et al.
Omid Mehrpour et al.
Background: Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking an...
A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants [0.03%]
一种用于准确识别婴儿颅脑损伤的可解释决策规则的深度神经网络框架
Baiming Zou,Xinlei Mi,Elizabeth Stone et al.
Baiming Zou et al.
Objective: We aimed to develop a robust framework to model the complex association between clinical features and traumatic brain injury (TBI) risk in children under age two, and identify significant features to derive cli...