Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare [0.03%]
不确定性感知机器学习在医疗保健中的展望
Andrea Campagner,Elia Mario Biganzoli,Clara Balsano et al.
Andrea Campagner et al.
The integration of machine learning (ML) into healthcare is accelerating, driven by the proliferation of biomedical data and the promise of data-driven clinical support. A key challenge in this context is managing the pervasive uncertainty ...
Development of a machine learning-based prognostic model for survival prediction in patients with lung cancer brain metastases using multicenter clinical data [0.03%]
基于机器学习的肺癌脑转移患者生存预测模型的多中心临床数据开发
Yuyan Xie,Xuqin Xiang,Menglin Fan et al.
Yuyan Xie et al.
Methods: Accurate prognosis prediction for lung cancer brain metastasis (LCBM) patients is critical for clinical decision-making. This study integrates data from the SEER database (n = 2624) and Harbin Medical University ...
Project Management Digitalisation of the Clinical Research at the University Medical Centre: Good Practice of using REDCap as a Digitalisation Tool [0.03%]
基于REDcap的临床研究项目管理数字化及其应用实践——以大学医学中心为例
Zdenko Garašević,Franc Strle,Martina Jaklič
Zdenko Garašević
Objective: The digital tool REDCap (Research Electronic Data Capture) was implemented at the University Medical Centre Ljubljana (UMCL) with the goal of digitalising and streamlining research processes. This study aimed t...
Enhancing leukemia detection in medical imaging using deep transfer learning [0.03%]
基于深度迁移学习的医学影像白血病检测增强技术
Afeez A Soladoye,David B Olawade,Ibrahim A Adeyanju et al.
Afeez A Soladoye et al.
Background: Acute Lymphoblastic Leukemia (ALL) is the most common pediatric cancer, requiring early detection to save lives and reduce the financial burden of advanced-stage treatment. While traditional diagnostic methods...
Psychometric properties of an Iranian instrument for assessing adherence to ethical principles in the use of artificial intelligence among healthcare providers [0.03%]
伊朗医疗保健提供者使用人工智能伦理原则遵从性评估工具的心理测量属性
Mohsen Khosravi,Yasaman Herandi,Sedighe Sadat Tabatabaei Far et al.
Mohsen Khosravi et al.
Introduction: Artificial Intelligence (AI) technologies, especially machine learning and deep learning, are increasingly utilized to improve diagnostic accuracy and treatment selection in healthcare. The aim of this study...
IBDAIM:Artificial intelligence for analyzing intestinal biopsies pathological images for assisted integrated diagnostic of inflammatory bowel disease [0.03%]
IBDAIM：用于分析肠道活检病理图像的炎症性肠病辅助综合诊断人工智能系统
Chengfei Cai,Qianyun Shi,Mingxin Liu et al.
Chengfei Cai et al.
Background: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is challenging to diagnose accurately from pathological images due to its complex histological features. This study...
Improving personalized healthcare with automated longitudinal EHR analysis [0.03%]
基于自动化纵向EHR分析的精准医疗研究
Gautam Pal
Gautam Pal
Background: Traditional Electronic Health Record (EHR) data analysis at King's College Hospital relies on extensive manual effort, from data extraction to reporting, limiting efficiency and scalability. This study present...
An analytical evaluation of contact tracing systems using real-world individual-level data [0.03%]
基于真实世界个体水平数据的追溯系统分析评估
Yuanxia Li,Kacey C Ernst,Kristen Pogreba-Brown et al.
Yuanxia Li et al.
Background: Contact tracing plays an important role in controlling contagious diseases. During the COVID-19 pandemic, new contact tracing systems with automation have been developed and adopted. Nevertheless, due to the p...
A two-stage machine learning-based risk assessment model for intravenous thrombolysis in acute ischemic stroke (AIS): A multi-center modeling study of pooled datasets [0.03%]
基于机器学习的急性缺血性卒中静脉溶栓两阶段风险评估模型：多中心建模研究及数据集融合分析
Shudan Zhu,Qing Ye,Hong Wu
Shudan Zhu
Objective: Develop a two-stage, machine learning-based thrombolysis risk stratification model from existing medical datasets and electronic health records to predict the risk of early hemorrhagic transformation(HT) and in...
Enhancing rare disease detection with deep phenotyping from EHR narratives: evaluation on Jeune syndrome [0.03%]
基于EHR叙述的深度表型在罕见病检测中的应用——以Jeune综合征为例
Carole Faviez,Xiaomeng Wang,Marc Vincent et al.
Carole Faviez et al.
Background: Patients with rare diseases frequently experience misdiagnoses and long diagnostic delays. Accelerating their diagnosis is essential to ensure timely access to appropriate care. Given the increasing availabili...