Temporal Entity Extractor

This package implements temporal entity extraction for spaCy/medspaCy styled documents. It is compatible with spaCy-styled tokenizer. This package contains two steps:

1. token tagging: label tokens according to the concept rules
2. entity extraction: label temporal entities based on the token labels.

Examples

1. Token tagging with concept rules

import medspacy
import spacy
from medspacy.ner import TargetRule
from medspacy.ner import ConceptTagger
from spacy.tokens import Span

import temporal_token_rules

nlp = medspacy.load(medspacy_enable=["medspacy_pyrush"]) #sentenciser
concept_matcher = nlp.add_pipe("medspacy_concept_tagger")
tagger_rules = temporal_token_rules.get_token_rules()
concept_matcher.add(tagger_rules) 

doc_token_tagger = nlp("for the past 2 weeks")
for token in doc_token_tagger:
    print(token, token._.concept_tag)
    
>>> 
for 
the DETERMINER
past PAST
2 
weeks WEEK

2. Temporal entity extraction based on token

import temporal_NER_rules
if "medspacy_target_matcher" in nlp.pipe_names:
    nlp.remove_pipe("medspacy_target_matcher")
target_matcher = nlp.add_pipe("medspacy_target_matcher")
ner_rules = temporal_NER_rules.get_NER_rules()
target_matcher.add(ner_rules)
doc = nlp(test_string)
visualize_ent(doc)

Publication

This work has been submitted to AMIA 2025 poster session: "Streamlining Temporal Information Extraction: Integrating Rule-Based Methods into MedspaCy for Clinical Application, Mengke Hu, Alec B. Chapman, Patrick R. Alba, Jianlin Shi"