Extended Methods and Software Development for Health NLP

健康 NLP 的扩展方法和软件开发

• 批准号: 9421556
• 负责人: NOEMIE ELHADAD
• 金额: $ 79.35万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9421556
• 关键词: Apache; Benchmarking; Big Data; Caring; Characteristics; Clinical; Communication; Communities; Complex; Computer software; Data; Data Science; Event; Foundations; Goals; Gold; Health; Information Resources; Institution; International; Investigation; Knowledge; Knowledge Extraction; Link; Literature; Marshal; Medicine; Methods; Names; Natural Language Processing; Ontology; Patients; Personal Satisfaction; Phenotype; Philosophy; Public Health; Research; Semantics; Solid; Standardization; Stream; Supervision; System; Terminology; Text; Translating; Translational Research; Vision; Work; commercialization; design; health knowledge; improved; information organization; method development; novel; point of care; portability; precision medicine; programs; social media; software development; success; syntax; tool; translational medicine

PROJECT SUMMARY There is a deluge of health-related texts in many genres, from the clinical narrative to newswire and social media. These texts are diverse in content, format, and style, and yet they represent complementary facets of biomedical and health knowledge. Natural Language Processing (NLP) holds much promise to extract, understand, and distill valuable information from these overwhelming large and complex streams of data, with the ultimate goal to advance biomedicine and impact the health and wellbeing of patients. There have been a number of success stories in various biomedical NLP applications, but the NLP methods investigated are usually tailored to one specific phenotype and one institution, thus reducing portability and scalability. Moreover, while there has been much work in the processing of clinical texts, other genres of health texts, like narratives and posts authored by health consumers and patients, are lacking solutions to marshal and make sense of the health information they contain. Robust NLP solutions that answer the needs of biomedicine and health in general have not been fully investigated yet. A unified, data-science approach to health NLP enables the exploration of methods and solutions unprecedented up to now. Our vision is to unravel the information buried in the health narratives by advancing text-processing methods in a unified way across all the genres of texts. The crosscutting theme is the investigation of methods for health NLP (hNLP) made possible by big data, fused with health knowledge. Our proposal moves the field into exploring semi-supervised and fully unsupervised methods, which only succeed when very large amounts of data are leveraged and knowledge is injected into the methods with care. Our hNLP proposal also targets a key challenge of current hNLP research: the lack of shared software. We seek to provide a clearinghouse for software created under this proposal, and as such all developed tools will be disseminated. Starting from the data characteristics of health texts and information needs of stakeholders, we will develop and evaluate methods for information extraction, information understanding. We will translate our research into the publicly available NLP software platform cTAKES, through robust modules for extraction and understanding across all genres of health texts. We will also 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, to participatory medicine. Finally, we will disseminate our work through community activities, such as challenges to advance the state of the art in health natural language processing.

项目概要 从临床叙述到新闻专线和社交媒体，有大量与健康相关的文本，涉及多种类型。 媒体。这些文本在内容、格式和风格上各不相同，但它们代表了互补的方面。 生物医学和健康知识。自然语言处理 (NLP) 有望提取、 从这些巨大而复杂的数据流中理解并提取有价值的信息， 最终目标是推进生物医学并影响患者的健康和福祉。曾经有过一个 各种生物医学 NLP 应用的成功案例很多，但研究的 NLP 方法是 通常针对一种特定表型和一种机构量身定制，从而降低了可移植性和可扩展性。 此外，虽然在临床文本的处理方面做了很多工作，但其他类型的健康文本，例如 由健康消费者和患者撰写的叙述和帖子缺乏整理和制定的解决方案 了解它们所包含的健康信息。强大的 NLP 解决方案可满足生物医学和 总体健康状况尚未得到充分调查。健康 NLP 的统一数据科学方法使 对方法和解决方案的探索是迄今为止前所未有的。 我们的愿景是通过推进文本处理来揭示隐藏在健康叙述中的信息 以统一的方式涵盖所有文本类型的方法。横切主题是方法的研究 大数据与健康知识的融合使健康自然语言处理 (hNLP) 成为可能。我们的提案推动了这一领域的发展 探索半监督和完全无监督的方法，这些方法只有在数量非常大时才能成功 利用大量数据并将知识小心地注入到方法中。我们的 hNLP 提案还针对 当前 hNLP 研究的关键挑战：缺乏共享软件。我们寻求提供一个信息交换所 根据该提案创建的软件以及所有开发的工具都将得到传播。从 健康文本的数据特征和利益相关者的信息需求，我们将开发和评估 信息提取、信息理解的方法。我们将把我们的研究成果公开 可用的 NLP 软件平台 cTAKES，通过强大的模块进行跨领域的提取和理解 健康文本的类型。我们还将通过几个用例展示我们的方法和工具的影响， 从临床护理到公共卫生，到转化医学和精准医学，再到参与性医学 药品。最后，我们将通过社区活动传播我们的工作，例如推进挑战 健康自然语言处理领域的最先进技术。