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

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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.