Community perspectives on the use of electronic health data to support reflective practice by health professionals [0.03%]
社区成员视角：利用电子健康数据支持卫生专业人员反思实践
Anna Janssen,Kavisha Shah,Melanie Keep et al.
Anna Janssen et al.
Background: Electronic health records and other clinical information systems have crucial roles in health service delivery and are often utilised for patient care as well as health promotion and research. Government agenc...
Prediction of 30-day mortality for ICU patients with Sepsis-3 [0.03%]
基于SEPSIS-3标准的ICU内重症感染患者的30天死亡率预测模型研究
Zhijiang Yu,Negin Ashrafi,Hexin Li et al.
Zhijiang Yu et al.
Background: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mo...
A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy [0.03%]
基于机器学习算法的永久性结肠造口术后腰疝风险预测模型
Tian Dai,Manzhen Bao,Miao Zhang et al.
Tian Dai et al.
Objective: To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups...
Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation [0.03%]
基于深度学习的表面心电图和临床特征在预测导管消融后房颤复发中的多模态融合研究
Yue Qiu,Hongcheng Guo,Shixin Wang et al.
Yue Qiu et al.
Background: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on ...
Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning [0.03%]
基于可解释性人工智能的深度学习集成方法在肺癌和结肠癌分类中的应用及超参数优化
K Vanitha,Mahesh T R,S Sathea Sree et al.
K Vanitha et al.
Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate ...
An improved data augmentation approach and its application in medical named entity recognition [0.03%]
一种改进的数据增强方法及其在医学命名实体识别中的应用
Hongyu Chen,Li Dan,Yonghe Lu et al.
Hongyu Chen et al.
Performing data augmentation in medical named entity recognition (NER) is crucial due to the unique challenges posed by this field. Medical data is characterized by high acquisition costs, specialized terminology, imbalanced distributions, ...
Improving the quality of Persian clinical text with a novel spelling correction system [0.03%]
一种新型拼写纠正系统在改善波斯语临床文本质量方面的应用
Seyed Mohammad Sadegh Dashti,Seyedeh Fatemeh Dashti
Seyed Mohammad Sadegh Dashti
Background: The accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. The Persian language, with its abundant vocabulary and comp...
Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania [0.03%]
解锁治疗成功：预测躁狂症青少年非典型抗精神病药物的延续性使用
Xiangying Yang,Wenbo Huang,Li Liu et al.
Xiangying Yang et al.
Purpose: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discov...
Joint extraction of Chinese medical entities and relations based on RoBERTa and single-module global pointer [0.03%]
基于RoBERTa和单模块全局指针的中文医学实体及关系联合抽取方法
Dongmei Li,Yu Yang,Jinman Cui et al.
Dongmei Li et al.
Background: Most Chinese joint entity and relation extraction tasks in medicine involve numerous nested entities, overlapping relations, and other challenging extraction issues. In response to these problems, some traditi...
An ontology-based tool for modeling and documenting events in neurosurgery [0.03%]
基于本体的神经外科事件建模和记录工具
Patricia Romao,Stefanie Neuenschwander,Chantal Zbinden et al.
Patricia Romao et al.
Background: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potential...