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:
- 金额:$ 50.35万
- 依托单位:
- 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:
- 关键词:Active LearningAddressAdoptedAdoptionAlgorithmsArchitectureAttentionBeliefClinicalClinical ResearchClosure by clampCloud ComputingCommunitiesCustomDataDevelopmentDiagnosisElectronic Health RecordEnvironmentFast Healthcare Interoperability ResourcesGenerationsGrantGrowthHealth SciencesHealthcareHospital AdministrationInternationalLanguageLicensingMachine LearningMedicalModelingNatural Language ProcessingNatural Language Processing pipelineOperations ResearchOutputPatientsPerformancePsychological TransferRecordsResearchServicesSystemTechnologyTexasTimeTranslational ResearchUniversitiesWorkactive methodbaseclinical applicationclinical databasecloud basedcommercializationcostdata modelingdeep learningdeep learning algorithmexperienceimprovedinsightinteroperabilitylanguage traininglearning algorithmmodel buildingnext generationnovelpreventrapid growthtooluser-friendlyweb 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.
项目摘要
电子健康记录的广泛采用(EHR)导致了巨大的临床数据库,这使得能够快速
医疗分析市场的增长。分析EHRS数据的一个特别挑战是如此详细
患者信息嵌入到临床文件中,而不是直接用于下游分析。
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的关注,到2021年,全球市场估计为26.5亿美元。在我们的
以前的工作,我们已经开发了夹具(临床语言注释,建模和处理),这是一种临床
NLP工具具有通过多个国际NLP挑战证明表现出色的表现
用户社区(从700多个组织中的用户下载超过1,500个)。夹具的商业化
Melax Technologies Inc.取得了成功(即,现在有十几个获得许可的客户);但这也揭示了它的
限制是云时代的桌面应用程序。因此,我们建议将夹具扩展到新的云 -
基于服务面向服务的平台(称为Clamp-CS),该平台将通过:1)解决确定的挑战
通过利用最新算法来提高临床NLP性能并降低注释成本
例如深度学习,积极学习和转移学习,并使他们可以通过经验不足的人访问
用户; 2)遵循新的面向服务的体系结构,使夹具C可以通过SaaS和Paas提供,准备就绪
用于基于云的开发和部署; 3)改善夹具-CS的互操作性与下游
遵循两个广泛使用标准表示的应用:HL7 FHIR(快速医疗保健互操作性
资源)和OMOP CMD(常见数据模型),以支持临床操作和研究中的用例
分别。借助这些高级功能,我们认为夹具CS将是领先的临床NLP系统
市场,它将加速使用NLP技术来用于多样化的医疗保健应用和
临床/翻译研究。
项目成果
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