The advent of large, open access text databases has driven advances in state- of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data availabl
The advent of large, open access text databases has driven advances in state- of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data availabl
MIMIC-IV-Note数据集是由斯坦福大学从Beth Israel Deaconess Medical Center收集的331,794份去标识化出院总结中提取的,包含270,033对临床笔记和简短医院过程(BHC)摘要。该数据集专为BHC摘要任务设计,强调临床笔记与相应BHC之间的关系。创建此基准数据集对于复制研究结果和比较未来工作至关重要。数据集的应用领域是自动化或加速BHC笔记生成,解决
The advent of large, open access text databases has driven advances in state- of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data availabl