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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要电子健康记录 (EHR) 的广泛采用催生了庞大的临床数据库，这使得快速诊断成为可能。医疗保健分析市场的增长。分析 EHR 数据的一项特殊挑战是详细程度患者信息嵌入临床文档中，不能直接用于下游分析。因此，临床自然语言处理（NLP）技术可以解锁嵌入的信息临床叙述受到了极大关注，预计到 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） NLP 工具在多项国际 NLP 挑战赛和大量研究中展现出卓越的性能用户社区（来自 700 多个组织的用户超过 1,500 次下载）。 CLAMP 的商业化 Melax Technologies Inc. 取得了成功（即现在拥有十几个授权客户）；但它也揭示了它的云时代桌面应用程序的局限性。因此，我们建议将 CLAMP 扩展到新的 Cloud- 基于服务的平台（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，并让经验不足的人也能接触到它们用户； 2) 遵循新的面向服务的架构，通过 SaaS 和 PaaS 提供 CLAMP-CS，做好准备用于基于云的开发和部署； 3) 提高 CLAMP-CS 与下游的互操作性应用程序遵循两种广泛使用的标准表示形式： HL7 FHIR（快速医疗互操作性资源）和 OMOP CMD（通用数据模型），以支持临床操作和研究中的用例分别。凭借这些先进的功能，我们相信 CLAMP-CS 将成为该领域领先的临床 NLP 系统市场，它将加速 NLP 技术在各种医疗保健应用中的采用临床/转化研究。