SBIR Phase I: Development of a fully annotated corpus for the training of a Clinical Question Answering System for critical results delivery at the Point of Care

SBIR 第一阶段：开发一个完整注释的语料库，用于培训临床问答系统，以便在护理点交付关键结果

• 批准号: 2014686
• 负责人: Robert Grzeszczuk
• 金额: $ 22.5万
• 依托单位: IN CONTEXT REPORTING INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014686&HistoricalAwards=false
• 关键词: SBIR; Phase; Development; fully; annotated

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from improving the quality of healthcare and streamlining its delivery. The accumulation of clinical data has become a potentially valuable resource for clinical practice, as Electronic Medical Records (EMRs) contain information on day-to-day patient care. Latest Natural Language Processing (NLP) techniques applied to EMR data enable the development of health Intelligent Virtual Assistants (hIVAs) to assist healthcare professionals in incorporating evidence-based decision support, reducing errors and improving efficiency. Current most promising NLP approaches are underdeveloped for the clinical domain given the lack of high-quality annotated clinical data required for training, testing and validating the machine learning algorithms. As most EMR data is available as unstructured free text, software developers in Artificial Intelligence (AI) struggle to find these annotated texts. The proposed project will inform the production of high-quality hIVAs - from voice-based clinical AI chatbots for assisting physicians at the point of care to Question-Answering systems for clinical decision-making. This Small Business Innovation Research (SBIR) Phase I project addresses the technical challenge of exploiting different combinations of Deep Learning (DL) structures for developing a novel set of annotation tools and an expert adjudication methodology to optimize the development of annotated corpora, specifically tailored for the clinical domain. The lack of these standard and annotated data sets is a major bottleneck preventing progress in clinical Information Extraction. Without these corpora, individual Natural Language Processing applications abound without the ability to train different algorithms, share and integrate modules, or compare performance. The company is leveraging the latest DL techniques to develop a unique architecture, able to identify a comprehensive set of context modifiers within unstructured clinical texts. This approach will boost the semi-automatic annotation of clinical corpora; produce accurate and robust annotated corpora; and reduce corpora production time and cost. The project objectives include: (1) adapting the existing in-house algorithm for automatic clinical text pre-annotation; (2) integrating a hybrid algorithm into a multi-user operable software platform for obtaining a minimum viable semi-automatic annotation product; (3) conducting a small pilot study to validate the performance of the resulting software platform and a Minimum Viable Product of an annotated corpus for diagnostic imaging reports.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响将是由于改善医疗保健质量和简化其交付的影响而产生的。临床数据的积累已成为临床实践的潜在宝贵资源，因为电子病历（EMRS）包含有关日常患者护理的信息。最新的自然语言处理（NLP）技术应用于EMR数据，使健康智能虚拟助手（HIVAS）的发展能够帮助医疗保健专业人员合并基于证据的决策支持，降低错误并提高效率。鉴于缺乏培训，测试和验证机器学习算法所需的高质量注释临床数据，目前最有前途的NLP方法对于临床领域而言是不发达的。由于大多数EMR数据可作为非结构化的免费文本使用，因此人工智能（AI）的软件开发人员都难以找到这些带注释的文本。拟议的项目将为高质量Hivas的生产提供信息 - 从基于语音的临床AI聊天机器人来协助医生在护理点到提问系统进行临床决策。这项小型企业创新研究（SBIR）I阶段项目解决了利用深度学习（DL）结构组合的技术挑战，以开发一套新型的注释工具和专家裁决方法，以优化注释的语料库的开发，专门针对临床领域量身定制。缺乏这些标准和注释的数据集是阻止临床信息提取进展的主要瓶颈。没有这些语料库，单个自然语言处理应用程序充满了培训不同算法，共享和集成模块或比较性能的能力。该公司利用最新的DL技术来开发独特的体系结构，能够在非结构化的临床文本中确定一组全面的上下文修饰符。这种方法将促进临床语料库的半自动注释；产生准确，健壮的注释语料库；并减少语料库的生产时间和成本。项目目标包括：（1）对现有的内部算法进行自动临床文本预通道； （2）将混合算法集成到多用户可操作的软件平台中，以获取最小可行的半自动注释产品； （3）进行一项小型试点研究，以验证所得的软件平台的性能以及带注释的诊断成像报告的注释语料库的最低可行产品。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来评估通过评估的支持。