README.md

In ML4LHS/medspacy: Medical Natural Language Processing using spaCy, scispacy, and negspacy

medspacy

The goal of medspacy is to perform biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python spaCy, scispacy, and negspacy packages.

Installation

You can install the GitHub version of medspacy with:

remotes::install_github('ML4LHS/medspacy')

Example

library(medspacy)
#> Importing spacy...
#> Importing scispacy...
#> Importing negspacy...
#> Loading the en_core_sci_sm language model...
#> Loading NegEx...
#> Loading the UMLS entity linker... (this may take a while)
#> Adding the UMLS entity linker and NegEx to the spacy pipeline...
#> 
#> Welcome to medspacy. Take a look at help(medspacy) to get started.

medspacy('This patient has diabetes and CKD stage 3 but no HTN.')
#>        cui      entity       lemma negated
#> 1 C0030705     patient     patient   FALSE
#> 2 C1578486     patient     patient   FALSE
#> 3 C1705908     patient     patient   FALSE
#> 4 C1578483     patient     patient   FALSE
#> 5 C1550655     patient     patient   FALSE
#> 6 C0011847    diabetes     diabete   FALSE
#> 7 C0011849    diabetes     diabete   FALSE
#> 8 C2316787 CKD stage 3 ckd stage 3   FALSE
#> 9 C0020538         HTN         htn    TRUE

ML4LHS/medspacy documentation built on Aug. 13, 2020, 12:26 a.m.