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期刊名:Bmc medical informatics and decision making

缩写:BMC MED INFORM DECIS

ISSN:N/A

e-ISSN:1472-6947

IF/分区:3.8/Q2

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共收录本刊相关文章索引3869
Clinical Trial Case Reports Meta-Analysis RCT Review Systematic Review
Classical Article Case Reports Clinical Study Clinical Trial Clinical Trial Protocol Comment Comparative Study Editorial Guideline Letter Meta-Analysis Multicenter Study Observational Study Randomized Controlled Trial Review Systematic Review
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...
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 ...
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...
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...
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 ...
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. ...
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...
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...
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...