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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响将来自于提高医疗保健质量和简化其提供。随着电子病历(EMR)包含有关患者日常护理的信息，临床数据的积累已成为临床实践的潜在有价值的资源。应用于电子病历数据的最新自然语言处理(NLP)技术使医疗智能虚拟助理(HIVA)的开发成为可能，以帮助医疗保健专业人员纳入循证决策支持，减少错误并提高效率。由于缺乏训练、测试和验证机器学习算法所需的高质量注释临床数据，目前最有希望的NLP方法在临床领域发展不足。由于大多数电子病历数据都是非结构化的自由文本，人工智能(AI)的软件开发人员很难找到这些带注释的文本。拟议的项目将为高质量HIVA的生产提供信息-从基于语音的临床AI聊天机器人，用于在护理点帮助医生，到用于临床决策的问答系统。这个小型企业创新研究(SBIR)第一阶段项目解决了利用深度学习(DL)结构的不同组合来开发一套新的注释工具和专家裁决方法以优化专门为临床领域定制的注释语料库的开发的技术挑战。缺乏这些标准的和带注释的数据集是阻碍临床信息提取进展的主要瓶颈。没有这些语料库，单独的自然语言处理应用程序比比皆是，无法训练不同的算法、共享和集成模块或比较性能。该公司正在利用最新的数字图书馆技术来开发一种独特的架构，能够在非结构化的临床文本中识别一组全面的上下文修饰语。这种方法将促进临床语料库的半自动标注，产生准确和健壮的标注语料库，并减少语料库的生成时间和成本。该项目的目标包括：(1)采用现有的用于自动临床文本预注释的内部算法；(2)将混合算法集成到多用户可操作的软件平台中，以获得最小可行的半自动注释产品；(3)进行小型试点研究，以验证所产生的软件平台的性能以及用于诊断成像报告的注释语料库的最低可行产品的性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.