Haematology dimension reduction, a large scale application to regular care haematology data [0.03%]
血细胞减少技术及其在大规模常规血液数据中应用研究
Huibert-Jan Joosse,Chontira Chumsaeng-Reijers,Albert Huisman et al.
Huibert-Jan Joosse et al.
Background: The routine diagnostic process increasingly entails the processing of high-volume and high-dimensional data that cannot be directly visualised. This processing may provide scaling issues that limit the impleme...
Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models [0.03%]
利用机器学习和深度学习模型分类和核查Twitter/X上与COVID-19相关的健康信息
Elham Sharifpoor,Maryam Okhovati,Mostafa Ghazizadeh-Ahsaee et al.
Elham Sharifpoor et al.
Background: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health info...
A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters [0.03%]
基于血液细胞参数的恶性血液病筛查的新方法及研究
Dehua Sun,Wei Chen,Jun He et al.
Dehua Sun et al.
Background: Screening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed an optimal screening model for malignant hematological diseases based o...
A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies [0.03%]
一种新颖的评估骑行运动状态的方法：结合深度学习和信号处理技术的探索性研究
Yingchun He,Yi-Haw Jan,Fan Yang et al.
Yingchun He et al.
This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity...
Synthesis of the clinical utilities and issues of intraoperative imaging devices in clinical reports: a systematic review and thematic synthesis [0.03%]
临床报告中术中影像设备的临床应用和问题：系统回顾和主题综合
Hiroyuki Suzuki,Yusuke Tsuboko,Manabu Tamura et al.
Hiroyuki Suzuki et al.
Background: Intraoperative imaging devices (i-ID), such as intraoperative optical coherence tomography (iOCT), offer surgeons critical insights previously unobservable, enhancing surgical precision and safety. Despite the...
Bayesian learning-based agent negotiation model to support doctor-patient shared decision making [0.03%]
基于bayes学习的代理人协商模型以支持医患共同决策
Xin Chen,Yong Liu,Fei-Ping Hong et al.
Xin Chen et al.
Background: Agent negotiation is widely used in e-commerce negotiation, cloud service service-level agreements, and power transactions. However, few studies have adapted alternative negotiation models to negotiation proce...
Development of minimum data set and electronic registry for hemodialysis patients management [0.03%]
血液透析患者管理系统最小数据集和电子病历的开发
Mahtab Karami,Ehsan Nabovati,Nasim Mirpanahi
Mahtab Karami
Background: This study aimed to develop a minimum dataset and an electronic registry system for hemodialysis patients to evaluate hemodialysis patients' treatment procedures and outcomes, conduct related research, and des...
Correction: Machine learning predicts pulmonary long Covid sequelae using clinical data [0.03%]
纠正：机器学习利用临床数据预测肺部长期新冠肺炎后遗症
Ermanno Cordelli,Paolo Soda,Sara Citter et al.
Ermanno Cordelli et al.
Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study [0.03%]
艰难的抉择：重症患者家属参与叶克膜治疗决策的经验描述性研究
Xiangying Yang,Yao Lin,Amao Tang et al.
Xiangying Yang et al.
Background: ECMO treatment for critically ill patients mostly requires family members to make surrogate decisions. However, the process and experience of family members' participation in decision making have not been well...
Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence [0.03%]
基于XGBoost、优化主成分分析和可解释人工智能的卒中风险预测模型构建及验证
Lesia Mochurad,Viktoriia Babii,Yuliia Boliubash et al.
Lesia Mochurad et al.
The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models i...