Sofie Holmeland,Tobias Blomberg,Andreas Mårtensson et al.
Sofie Holmeland et al.
Background: Semantic interoperability among health information systems (HISs), in particular electronic health records (EHRs), is crucial for informed healthcare decisions and patient access to vital health data. However,...
Harnessing Advanced Machine Learning Techniques for Microscopic Vessel Segmentation in Pulmonary Fibrosis Using Novel Hierarchical Phase-Contrast Tomography (HiP-CT) Images [0.03%]
利用新型 hierarchical phase-contrast tomography (HiP-CT) 图像中的高级机器学习技术进行肺纤维化微观血管分割研究
Pardeep Vasudev,Moucheng Xu,Mehran Azimbagarad et al.
Pardeep Vasudev et al.
Background: Fibrotic lung disease is a progressive illness that causes scarring and ultimately respiratory failure, with irreversible damage by the time its diagnosed on computed tomography imaging. Recent research postul...
Leveraging Guideline-Based Clinical Decision Support Systems with Large Language Models: A Case Study with Breast Cancer [0.03%]
基于指南的临床决策支持系统与大型语言模型结合在乳腺癌中的应用案例研究
Solène Delourme,Akram Redjdal,Jacques Bouaud et al.
Solène Delourme et al.
Background: Multidisciplinary tumor boards (MTBs) have been established in most countries to allow experts collaboratively determine the best treatment decisions for cancer patients. However, MTBs often face challenges su...
Deciphering abbreviations in Malaysian clinical notes using machine learning [0.03%]
利用机器学习解码马来西亚临床记录中的缩写
Ismat Mohd Sulaiman,Awang Bulgiba,Sameem Abdul Kareem et al.
Ismat Mohd Sulaiman et al.
Objective: This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a Natural Language Processing system...
Cross-lingual Natural Language Processing on Limited Annotated Case/Radiology Reports in English and Japanese: Insights from the Real-MedNLP Workshop [0.03%]
来自Real-MedNLP workshop的基于英日双语有限注释医疗文本的跨语言自然语言处理：案例和放射学报告分析
Shuntaro Yada,Yuta Nakamura,Shoko Wakamiya et al.
Shuntaro Yada et al.
Background: Textual datasets (corpora) are crucial for the application of natural language processing (NLP) models. However, corpus creation in the medical field is challenging, primarily because of privacy issues with r...
Ileana Montoya Perez,Parisa Movahedi,Valtteri Nieminen et al.
Ileana Montoya Perez et al.
Background: Synthetic data have been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the origina...
Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations [0.03%]
具有语义注释的欧洲最大的医学数据模型研究基础设施（策展）
Sarah Riepenhausen,Max Blumenstock,Christian Niklas et al.
Sarah Riepenhausen et al.
Background: Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. ...
Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments [0.03%]
基于膝关节侧位X线、人口统计信息和症状评估的深学习预测髌股骨骨关节炎进展
Neslihan Bayramoglu,Martin Englund,Ida K Haugen et al.
Neslihan Bayramoglu et al.
Objective: In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of 7 years. ...
Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse [0.03%]
自然语言处理算法的发展与验证在临床数据仓库背景下对文档进行匿名化处理
Xavier Tannier,Perceval Wajsbürt,Alice Calliger et al.
Xavier Tannier et al.
Objective: The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the diff...
Report from the 68th GMDS Annual Meeting: Science. Close to People [0.03%]
第68届德国医学实验室学会年会报告：科学服务于大众
Jonas Bienzeisler,Ariadna Perez-Garriga,Lea C Brandl et al.
Jonas Bienzeisler et al.