CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines

TLDR

Existing clinical NLP systems such as MetaMap and cTAKES have been used for information extraction, yet users often must customize them, requiring substantial NLP expertise. CLAMP is a new toolkit that offers state‑of‑the‑art NLP components and a user‑friendly graphical interface to enable rapid construction of customized pipelines. By providing modular components and a drag‑and‑drop GUI, CLAMP allows users to assemble pipelines tailored to their specific applications without deep NLP knowledge. In evaluations, CLAMP’s default pipeline achieved strong named‑entity recognition and concept‑encoding performance, and the GUI efficiently built high‑performance pipelines for smoking‑status and lab‑test extraction; the toolkit is publicly available and a valuable resource for the clinical NLP community.

Abstract

Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community.