Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration [0.03%]
协调电子健康记录以实现可重复研究：来自英国COVID-19研究协作体的挑战、解决方案和建议
Hoda Abbasizanjani,Fatemeh Torabi,Stuart Bedston et al.
Hoda Abbasizanjani et al.
Background: The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Be...
Jie Zhang,Zong-Ming Zhang
Jie Zhang
Background: The growing application of artificial intelligence (AI) in healthcare has brought technological breakthroughs to traditional diagnosis and treatment, but it is accompanied by many risks and challenges. These a...
A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases [0.03%]
在间歇性疾病中共同描述加重发生率，持续时间和严重程度的参数模型
Abdollah Safari,John Petkau,Mark J FitzGerald et al.
Abdollah Safari et al.
Background: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exac...
Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital [0.03%]
基于概念搜索的机器学习临床决策支持系统：在挪威医院进行实地试验
G T Berge,O C Granmo,T O Tveit et al.
G T Berge et al.
Background: Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate importan...
Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration [0.03%]
哮喘和慢性阻塞性肺病的真实世界数据库的开发：SingHealth-Duke-NUS-GSK COPD 和哮喘真实世界证据 (SDG-CARE) 协作组
Sean Shao Wei Lam,Andrew Hao Sen Fang,Mariko Siyue Koh et al.
Sean Shao Wei Lam et al.
Purpose: The SingHealth-Duke-GlaxoSmithKline COPD and Asthma Real-world Evidence (SDG-CARE) collaboration was formed to accelerate the use of Singaporean real-world evidence in research and clinical care. A centerpiece of...
Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data [0.03%]
通过多模态深度学习分析患者的结构化和非结构化健康数据来预测减压手术
Chethan Jujjavarapu,Pradeep Suri,Vikas Pejaver et al.
Chethan Jujjavarapu et al.
Background: Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Pati...
Stratifying non-small cell lung cancer patients using an inverse of the treatment decision rules: validation using electronic health records with application to an administrative database [0.03%]
使用治疗决策规则的逆规则对非小细胞肺癌患者进行分层：使用电子健康记录进行验证，并应用于行政数据库
Min-Hyung Kim,Sojung Park,Yu Rang Park et al.
Min-Hyung Kim et al.
Background: To validate a stratification method using an inverse of treatment decision rules that can classify non-small cell lung cancer (NSCLC) patients in real-world treatment records. ...
Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care [0.03%]
关于急性病和老年病特征的信息对重症监护中老年患者生命维持治疗决策的贡献
Michael Beil,P Vernon van Heerden,Dylan W de Lange et al.
Michael Beil et al.
Background: Life-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where furthe...
Application of machine learning techniques for predicting survival in ovarian cancer [0.03%]
机器学习技术在卵巢癌生存预测中的应用
Amir Sorayaie Azar,Samin Babaei Rikan,Amin Naemi et al.
Amir Sorayaie Azar et al.
Background: Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on s...
Transferability and interpretability of the sepsis prediction models in the intensive care unit [0.03%]
ICU中脓毒症预测模型的可转移性和可解释性
Qiyu Chen,Ranran Li,ChihChe Lin et al.
Qiyu Chen et al.
Background: We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the dep...