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) <doi:10.18653/v1/W19-5034>. The 'medspacy' package uses 'ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept embeddings developed by Beam (2018) <arXiv:1804.01486>.
|Author||Karandeep Singh [aut, cre], Benjamin Kompa [aut], Andrew Beam [aut], Allen Schmaltz [aut]|
|Maintainer||Karandeep Singh <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.