Clinical Natural Language Processing using 'spaCy', 'scispaCy', and 'medspaCy'

Performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the 'scispaCy' package is described by Neumann (2019) . The 'medspacy' package uses 'ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) . Clinspacy also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept embeddings developed by Beam (2018) .


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1.0.2 by Karandeep Singh, 10 months ago

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Authors: Karandeep Singh [aut, cre] , Benjamin Kompa [aut] , Andrew Beam [aut] , Allen Schmaltz [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports reticulate, data.table, assertthat, rappdirs, utils, magrittr

Suggests knitr, rmarkdown

See at CRAN