Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation [0.03%]
一种预测重度患者术后住院时长的机器学习模型的可解释性研究：开发与评估
Ha Na Cho,Imjin Ahn,Hansle Gwon et al.
Ha Na Cho et al.
Background: Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importa...
Muntaha Tabassum,Saba Mahmood,Amal Bukhari et al.
Muntaha Tabassum et al.
Background: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider try...
Modified multiscale Renyi distribution entropy for short-term heart rate variability analysis [0.03%]
改进的多尺度Renyi分布熵在短期心率变异性分析中的应用
Manhong Shi,Yinuo Shi,Yuxin Lin et al.
Manhong Shi et al.
Background: Multiscale sample entropy (MSE) is a prevalent complexity metric to characterize a time series and has been extensively applied to the physiological signal analysis. However, for a short-term time series, the ...
Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network [0.03%]
基于Bayesian网络的非体外循环冠状动脉旁路移植术后急性缺血性脑卒中的风险因素及预测模型
Wenlong Zou,Haipeng Zhao,Ming Ren et al.
Wenlong Zou et al.
Background: This study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian networ...
Paramedic perceptions of decision-making when managing mental health-related presentations: a qualitative study [0.03%]
关于处理与心理健康相关的病例时的决策过程：急救人员的认知定性研究
Kate Emond,George Mnatzaganian,Michael Savic et al.
Kate Emond et al.
Background: Mental health presentations account for a considerable proportion of paramedic workload; however, the decision-making involved in managing these cases is poorly understood. This study aimed to explore how para...
DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction [0.03%]
基于疾病临床和分子关联的多视图图对比网络促进疾病进展预测(DAPNet)
Haoyu Tian,Xiong He,Kuo Yang et al.
Haoyu Tian et al.
Background: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease ...
Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning [0.03%]
基于机器学习的早产儿喂养不耐受风险预测模型的构建及SHAP可解释性分析
Hui Xu,Xingwang Peng,Ziyu Peng et al.
Hui Xu et al.
Objective: To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis. ...
A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach [0.03%]
基于临床常规实验室指标的机器学习预测非小细胞肺癌预后的模型研究
Yuli Wang,Na Mei,Ziyi Zhou et al.
Yuli Wang et al.
Background: Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predi...
Classification of lumbar spine disorders using large language models and MRI segmentation [0.03%]
基于大型语言模型和MRI分割的腰椎疾病分类
Rongpeng Dong,Xueliang Cheng,Mingyang Kang et al.
Rongpeng Dong et al.
Background: MRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar s...
Evaluating the usability of Iran's national comprehensive health information system: a think-aloud study to uncover usability problems in the recording of childcare data [0.03%]
伊朗国家级综合卫生信息系统使用性评估：一项think aloud研究揭露儿童保健数据记录中的使用问题
Razieh Farrahi,Ehsan Nabovati,Reyhane Bigham et al.
Razieh Farrahi et al.
Introduction: Health information systems play a crucial role in the delivery of efficient and effective healthcare. Poor usability is one of the reasons for their lack of acceptance and low usage by users. The aim of this...