Extended Methods and Software Development for Health NLP

项目摘要 我们的计划愿景是通过推进文本处理来解开隐藏在健康相关叙述中的信息 方法以统一的方式跨越所有类型的健康文本，并通过先进的NLP分发它们 软件平台在坚实的治理和可持续性。交叉主题是对 健康NLP的方法通过大数据与健康知识融合成为可能。这件事背后的主题 更新是发展方法，使其在 现代机器学习技术的背景，特别是实现注意力机制的模型， 使用大型未标记数据集。深度学习方法在健康领域的渗透率越来越高 自然语言处理我们的建议旨在解决关键的方法差距和研究不足的领域 在当前前所未有的快节奏环境中。因此，我们的更新布局新颖， 健康NLP研究的探索，我们将通过我们的具体目标推进。我们的数据集将继续 以涵盖健康相关数据的范围-电子病历临床叙述，患者撰写- 在线社区帖子和与健康相关的社交媒体。我们将开发的方法的评估将是 执行概念提取、关系提取和表型比较的关键临床任务 其他传统或深度学习算法作为基准。我们将展示我们的方法的影响， 从临床护理点到公共卫生，再到转化和精确性， 药最后，我们将通过社群活动来传播我们的工作，以促进 健康自然语言处理

Project Summary Our program vision is to unravel the information buried in health-related narratives by advancing text-processing methods in a unified way across all the genres of health texts and distributing them through an advanced NLP software platform under solid governance and sustainability. The crosscutting theme is the investigation of methods for health NLP made possible by big data, fused with health knowledge. The underlying theme of this renewal is the development of methods towards generalizable, efficient and knowledge-rich models in the context of modern machine learning techniques, particularly models implementing attention mechanisms and using large unlabeled datasets. There is growing penetration of deep learning approaches in the field of health natural language processing. Our proposal aims to address critical methodological gaps and understudied areas in the current unprecedented fast-paced environment. Therefore, our renewal lays out novel and much needed explorations of health NLP research which we will advance through our specific aims. Our datasets will continue to span the spectrum of health-related data – Electronic Medical Records clinical narrative, patient-authored on- line community posts, and health-related social media. The evaluation of the methods we will develop will be performed on the key clinical tasks of concept extraction, relation extraction, and phenotyping with comparisons to other traditional or deep learning algorithms as baselines. We will demonstrate impact of our methods and tools through several use cases, ranging from clinical point of care to public health, to translational and precision medicine. Finally, we will disseminate our work through community activities to advance the state of the art in health natural language processing.