Heart failure classification using deep learning to extract spatiotemporal features from ECG [0.03%]
利用深度学习从心电图中提取时空特征对心力衰竭进行分类
Chang-Jiang Zhang,Yuan-Lu,Fu-Qin Tang et al.
Chang-Jiang Zhang et al.
Background: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public dat...
Automated machine learning for early prediction of acute kidney injury in acute pancreatitis [0.03%]
自动机器学习在急性胰腺炎中对急性肾损伤进行早期预测的应用
Rufa Zhang,Minyue Yin,Anqi Jiang et al.
Rufa Zhang et al.
Background: Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim...
Ruibin Wang,Kavisha Jayathunge,Rupert Page et al.
Ruibin Wang et al.
As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed t...
Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy [0.03%]
应用狄利克雷过程混合模型来填补反事实预测模型中缺失的预测数据：应用于2型糖尿病最优治疗预测
Pedro Cardoso,John M Dennis,Jack Bowden et al.
Pedro Cardoso et al.
Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions ...
A command centre implementation before and during the COVID-19 pandemic in a community hospital [0.03%]
社区医院在COVID-19大流行前和期间的指挥中心实施
Liza Grosman-Rimon,Pete Wegier,Ruben Rodriguez et al.
Liza Grosman-Rimon et al.
Introduction: The objective of the study was to assess the effects of high-reliability system by implementing a command centre (CC) on clinical outcomes in a community hospital before and during COVID-19 pandemic from the...
Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis [0.03%]
人工智能在从医学影像中检测淋巴瘤方面的表现：系统评价和荟萃分析
Anying Bai,Mingyu Si,Peng Xue et al.
Anying Bai et al.
Background: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagno...
Prediction of postoperative infectious complications in elderly patients with colorectal cancer: a study based on improved machine learning [0.03%]
结直肠癌老年患者术后感染并发症的预测：基于改进机器学习的研究
Yuan Tian,Rui Li,Guanlong Wang et al.
Yuan Tian et al.
Background: Infectious complications after colorectal cancer (CRC) surgery increase perioperative mortality and are significantly associated with poor prognosis. We aimed to develop a model for predicting infectious compl...
Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome [0.03%]
从对流感疫苗不良事件报告的标注中获得的经验教训以及与吉兰-巴雷综合症的关系
Madhuri Sankaranarayanapillai,Su Wang,Hangyu Ji et al.
Madhuri Sankaranarayanapillai et al.
Background: Vaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adver...
"VR is the future": perspectives of healthcare professionals on virtual reality as a diagnostic tool for dementia status in primary care [0.03%]
"虚拟现实是未来"：医疗专业人员对初级护理中使用虚拟现实作为诊断痴呆症工具的看法
Joshua Yondjo,Joyce Siette
Joshua Yondjo
Background: Healthcare professionals (HPs) hold critical perspectives on the barriers and facilitating factors for the implementation of virtual reality (VR) dementia diagnosis tools in the clinical setting. This study ai...
Andrew M Simms,Anshul Kanakia,Muhammad Sipra et al.
Andrew M Simms et al.
Background: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge gra...