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

By Ergin Soysal, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei V. S. Pakhomov, Hongfang Liu, Hua Xu. J. Am. Medical Informatics Assoc., 2017

343 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bff0b041b969e4d735c301923e0f736e8936642d

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Citations in the corpus

AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “[44] developed a clinical language annotation, modeling, and processing (CLAMP) toolkit for customized clinical NLP applications.”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “In [78], authors presented a toolkit named CLAMP that provides different state of the”
BioMRC: A Dataset for Biomedical Machine Reading Comprehension (2020, methodology): “used CLAMP (Soysal et al., 2017) to detect biomedical entities and link them to concepts of the UMLS Metathesaurus (Lindberg et al.”
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference (2020, unknown): “…to automatically and precisely curate the clinical case reports into structured knowledge, i.e. extract important clinical named entities and relationships from the narratives (Aronson and Lang 2010; Savova et al. 2010; Soysal et al. 2018; Caufield et al. 2019; Alfattni, Peek, and Nenadic 2020).”
A large language model for electronic health records (2022, background): “…concept extract are often rule-based, yet, most recent systems are based on machine learning models such as conditional random fields (CRFs) 50,51 , convolutional neural networks (CNN) 9,52 , and recurrent neural networks (RNN) implemented with long short-term memory strategy (LSTM) 10,53 .”