patient_processor_par: Process All Documents for One Patient

In CEDARS: Simple and Efficient Pipeline for Electronic Health Record Annotation

Description

Performs NLP annotations on all documents using previously established cluster, including NegEx and UMLS CUI tags.

Usage

patient_processor_par(
  cl,
  sub_corpus,
  text_format,
  nlp_engine,
  negex_simp,
  umls_selected,
  max_n_grams_length,
  negex_depth,
  single_core_model
)

Arguments

cl	Computing cluster.
sub_corpus	Data frame of text to annotate.
text_format	Text format.
nlp_engine	NLP engine, UDPipe only for now.
negex_simp	Simplifed negex.
umls_selected	Processed UMLS table.
max_n_grams_length	Maximum length of tokens for matching with UMLS concept unique identifiers (CUI's). Shorter values will result in faster processing. If ) is chosen, UMLS CUI tags will not be provided.
negex_depth	Maximum distance between negation item and token to negate. Shorter distances will result in decreased sensitivity but increased specificity for negation.
single_core_model	NLP model in case parallel processing is not used.

Value

NLP annotations dataframe.

CEDARS documentation built on Feb. 7, 2021, 5:06 p.m.