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

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.

Keywords: clinical text processing; machine learning; natural language processing.

Figure 1.

The user interface for building a pipeline in CLAMP.

Figure 2.

The interface in CLAMP for annotating entities and relations.

Figure 3.

The interface for selecting features and evaluation options for building machine learning–based NER models using CLAMP.