LBDiscover (version 0.1.0)

lsi_model: LSI model with enhanced biomedical term filtering and NLP verification

Description

This function implements an improved LSI model that more rigorously filters out non-biomedical terms from the results to ensure clinical relevance. It adds NLP-based validation as an additional layer of filtering.

Usage

lsi_model(
  term_doc_matrix,
  a_term,
  n_factors = 100,
  n_results = 100,
  enforce_biomedical_terms = TRUE,
  c_term_types = NULL,
  entity_types = NULL,
  validation_function = is_valid_biomedical_entity,
  min_word_length = 3,
  use_nlp = TRUE,
  nlp_threshold = 0.7
)

Value

A data frame with ranked discovery results.

Arguments

term_doc_matrix

A term-document matrix.

a_term

Character string, the source term (A).

n_factors

Number of factors to use in LSI.

n_results

Maximum number of results to return.

enforce_biomedical_terms

Logical. If TRUE, enforces strict biomedical term filtering.

c_term_types

Character vector of entity types allowed for C terms.

entity_types

Named vector of entity types (if NULL, will try to detect).

validation_function

Function to validate biomedical terms.

min_word_length

Minimum word length to include.

use_nlp

Logical. If TRUE, uses NLP-based validation for biomedical terms.

nlp_threshold

Numeric between 0 and 1. Minimum confidence for NLP validation.