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.

项目摘要 从临床叙事到新闻专线和社交媒体，许多类型的健康相关文本如潮水般涌来 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 生物医学和健康知识。自然语言处理（NLP）在提取、 从这些庞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。已经有 在各种生物医学NLP应用中有许多成功的故事，但研究的NLP方法是 通常针对一种特定的表型和一个机构，从而降低了可移植性和可扩展性。 此外，虽然在处理临床文本方面已经做了很多工作，但其他类型的健康文本，如 由健康消费者和患者撰写的叙述和帖子缺乏解决方案来整理和制作 了解它们所包含的健康信息。强大的NLP解决方案，满足生物医学和 一般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 方法和解决方案的探索是前所未有的。 我们的愿景是通过推进文本处理来解开隐藏在健康叙述中的信息 方法在一个统一的方式跨越所有体裁的文本。贯穿各领域的主题是方法的调查 健康NLP（hNLP）通过大数据与健康知识融合而成为可能。我们的提议 探索半监督和完全无监督的方法，这些方法只有在大量 数据的杠杆作用和知识注入的方法小心。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。我们寻求为以下方面提供一个信息交流中心： 根据这一提议开发的软件以及所有开发的工具都将分发。起 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之于众 可用的自然语言处理软件平台cTAKES，通过强大的模块提取和理解所有 健康类书籍。我们还将通过几个用例展示我们的方法和工具的影响， 从临床护理点到公共卫生，到转化和精准医学，再到参与式 药最后，我们将通过社群活动传播我们的工作，如挑战前进 健康自然语言处理的最新技术。