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交付。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 (EMR) 包含有关日常患者护理的信息。应用于 EMR 数据的最新自然语言处理 (NLP) 技术有助于开发健康智能虚拟助理 (hIVA)，以协助医疗保健专业人员纳入基于证据的决策支持、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带注释的临床数据，目前最有前途的 NLP 方法在临床领域的开发还不够。由于大多数 EMR 数据都以非结构化自由文本形式提供，人工智能 (AI) 领域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 - 从用于在护理点协助医生的基于语音的临床人工智能聊天机器人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颖的注释工具和专家评审方法的技术挑战，以优化专门针对临床领域定制的注释语料库的开发。缺乏这些标准和带注释的数据集是阻碍临床信息提取取得进展的主要瓶颈。如果没有这些语料库，单个自然语言处理应用程序就会大量存在，而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深度学习技术来开发一种独特的架构，能够识别非结构化临床文本中的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；生成准确且稳健的注释语料库；并减少语料库制作时间和成本。项目目标包括：（1）采用现有的内部算法进行自动临床文本预注释； (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) 进行一项小型试点研究，以验证所得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小可行产品。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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.