Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach [0.03%]
MARK-BE诊断巴雷特食管风险预测模型的开发和验证：一种病例对照机器学习方法
Avi Rosenfeld,David G Graham,Sarah Jevons et al.
Avi Rosenfeld et al.
Background: Screening for Barrett's Oesophagus (BE) relies on endoscopy which is invasive and has a low yield. This study aimed to develop and externally validate a simple symptom and risk-factor questionnaire to screen f...
CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction [0.03%]
基于CT的影像组学评分预测I、II期可切除非小细胞肺癌患者术后辅助化疗增益效果：一项多队列回顾性研究
Pranjal Vaidya,Kaustav Bera,Amit Gupta et al.
Pranjal Vaidya et al.
Background: Development and validation of a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (ES-NSCLC) that is prognostic of disease-free survival (DFS)...
A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service [0.03%]
英格兰国家卫生服务400万人的308种身心疾病的时空地图
Valerie Kuan,Spiros Denaxas,Arturo Gonzalez-Izquierdo et al.
Valerie Kuan et al.
Background: To effectively prevent, detect, and treat health conditions that affect people during their lifecourse, health-care professionals and researchers need to know which sections of the population are susceptible t...
Two-way mobile phone intervention compared with standard-of-care adherence support after second-line antiretroviral therapy failure: a multinational, randomised controlled trial [0.03%]
手机双向干预和标准抗病毒治疗后二线治疗失败的依从性支持的多国随机对照试验
Robert Gross,Justin Ritz,Michael D Hughes et al.
Robert Gross et al.
Background: Antiretroviral therapy (ART) non-adherence causes HIV treatment failure. Past behaviour might predict future behaviour; failing second-line ART could indicate ongoing risk for subsequent non-adherence. We aime...
Randomized Controlled Trial
The Lancet. Digital health. 2019 May;1(1):e26-e34. DOI:10.1016/S2589-7500(19)30006-8 2019
An image-based deep learning framework for individualizing radiotherapy dose [0.03%]
基于图像的深度学习框架实现放射治疗个体化剂量计算
Bin Lou,Semihcan Doken,Tingliang Zhuang et al.
Bin Lou et al.
Background: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (C...