CLAMP-CS: a Cloud-based, Service-oriented, high-performance Natural Language Processing Platform for Healthcare

CLAMP-CS：基于云、面向服务的高性能医疗自然语言处理平台

• 批准号: 10011177
• 负责人: Frank J. Manion
• 金额: $ 50.35万
• 依托单位: MELAX TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10011177
• 关键词: Active Learning; Address; Adopted; Adoption; Algorithms; Architecture; Attention; Belief; Clinical; Clinical Research; Closure by clamp; Cloud Computing; Communities; Custom; Data; Development; Diagnosis; Electronic Health Record; Environment; Fast Healthcare Interoperability Resources; Generations; Grant; Growth; Health Sciences; Healthcare; Hospital Administration; International; Language; Licensing; Machine Learning; Medical; Modeling; Natural Language Processing; Natural Language Processing pipeline; Operations Research; Output; Patients; Performance; Psychological Transfer; Records; Research; Services; System; Technology; Texas; Time; Translational Research; Universities; Work; active method; base; clinical application; clinical database; cloud based; commercialization; cost; data modeling; deep learning; deep learning algorithm; experience; improved; insight; interoperability; language training; learning algorithm; model building; next generation; novel; prevent; rapid growth; tool; user-friendly; web app

Project Summary Wide adoption of electronic health records (EHRs) has led to huge clinical databases, which enable the rapid growth of healthcare analytics market. One particular challenge for analyzing EHRs data is that much detailed patient information is embedded in clinical documents and not directly available for downstream analysis. Therefore, clinical natural language processing (NLP) technologies, which can unlock information embedded in clinical narratives, have received great attention, with an estimated global market of $2.65 billion by 2021 . In our previous work, we have developed CLAMP (Clinical Language Annotation, Modeling, and Processing), a clinical NLP tool with demonstrated superior performance through multiple international NLP challenges and a large user community (over 1,500 downloads by users from over 700 organizations). Commercialization of CLAMP by Melax Technologies Inc. has been successful (i.e., with a dozen licensed customers now); but it also reveals its limitations as a desktop application in the Cloud era. Therefore, we propose to extend CLAMP to a new Cloud- based, Service-oriented platform (called CLAMP-CS), which will address the identified challenges by: 1) improving clinical NLP performance and reducing annotation cost by leveraging the state-of-the-art algorithms such as deep learning, active learning and transfer learning and making them accessible to less experienced users; 2) following new service-oriented architectures to make CLAMP-CS available via SaaS and PaaS, ready for Cloud-based development and deployment; and 3) improving CLAMP-CS interoperability with downstream applications following two widely used standard representations: HL7 FHIR (Fast Healthcare Interoperability Resources) and OMOP CMD (Common Data Model), to support the use cases in clinical operations and research respectively. With these advanced features, we believe CLAMP-CS will be a leading clinical NLP system in the market and it will accelerate the adoption of NLP technology for diverse healthcare applications and clinical/translational research.

项目摘要 电子健康记录(EHR)的广泛采用导致了巨大的临床数据库，这使得快速 医疗保健分析市场的增长。分析EHR数据的一个特殊挑战是非常详细 患者信息嵌入在临床文档中，不能直接用于后续分析。 因此，临床自然语言处理(NLP)技术可以解锁嵌入在 临床叙事受到了极大的关注，据估计，到2021年，全球市场将达到26.5亿美元。在我们的 在之前的工作中，我们开发了CLAMP(临床语言注释、建模和处理)，一个临床语言注释，建模和处理 NLP工具，通过多个国际NLP挑战和大型 用户社区(700多个组织的用户超过1,500次下载)。夹具的商品化 Melax Technologies Inc.已经取得了成功(即现在有12个许可客户)；但它也透露了 在云时代作为桌面应用程序的局限性。因此，我们建议将CLAMP扩展到新的云- 基于面向服务的平台(称为CLAMP-CS)，它将通过以下方式解决已确定的挑战：1) 利用最先进的算法提高临床NLP性能并降低注释成本 例如深度学习、主动学习和迁移学习，并使经验较少人能够接触到它们 用户；2)遵循新的面向服务的体系结构，使CLAMP-CS可通过SaaS和PaaS使用，准备就绪 用于基于云的开发和部署；以及3)提高CLAMP-CS与下游的互操作性 遵循两种广泛使用的标准表示的应用：HL7 FHIR(快速医疗互操作性 资源)和OMOP CMD(公共数据模型)，以支持临床操作和研究中的用例 分别进行了分析。凭借这些先进的功能，我们相信CLAMP-CS将成为临床上领先的NLP系统 它将加快NLP技术在不同医疗保健应用中的采用，并 临床/转化性研究。