import logging
from spacy.matcher import Matcher
from spacy.tokens import Span
from spacy.tokens import Token
from medacy.pipeline_components.feature_overlayers.base import BaseOverlayer
[docs]class UnitOverlayer(BaseOverlayer):
"""
A pipeline component that tags units.
Begins by first tagging all mass, volume, time, and form units then aggregates as necessary.
"""
name="unit_annotator"
dependencies = []
def __init__(self, nlp):
self.nlp = nlp
Token.set_extension('feature_is_mass_unit', default=False, force=True)
nlp.entity.add_label('mass_unit')
Token.set_extension('feature_is_volume_unit', default=False, force=True)
nlp.entity.add_label('volume_unit')
Token.set_extension('feature_is_time_unit', default=False, force=True)
nlp.entity.add_label('time_unit')
Token.set_extension('feature_is_route_type', default=False, force=True)
nlp.entity.add_label('route_type')
Token.set_extension('feature_is_form_unit', default=False, force=True)
nlp.entity.add_label('form_unit')
Token.set_extension('feature_is_frequency_indicator', default=False, force=True)
nlp.entity.add_label('frequency_indicator')
Token.set_extension('feature_is_measurement_unit', default=False, force=True)
nlp.entity.add_label('measurement_unit')
Token.set_extension('feature_is_measurement', default=False, force=True)
nlp.entity.add_label('measurement')
Token.set_extension('feature_is_duration_pattern', default=False)
nlp.entity.add_label('duration_pattern')
self.mass_matcher = Matcher(nlp.vocab)
self.volume_matcher = Matcher(nlp.vocab)
self.time_matcher = Matcher(nlp.vocab)
self.route_matcher = Matcher(nlp.vocab)
self.form_matcher = Matcher(nlp.vocab)
self.unit_of_measurement_matcher = Matcher(nlp.vocab)
self.measurement_matcher = Matcher(nlp.vocab)
self.frequency_matcher = Matcher(nlp.vocab)
self.duration_matcher = Matcher(nlp.vocab)
self.mass_matcher.add('UNIT_OF_MASS', None,
[{'LOWER': 'mcg'}],
[{'LOWER': 'microgram'}],
[{'LOWER': 'micrograms'}],
[{'ORTH': 'mg'}],
[{'LOWER': 'milligram'}],
[{'LOWER': 'g'}],
[{'LOWER': 'kg'}],
[{'ORTH': 'mEq'}])
self.volume_matcher.add('UNIT_OF_VOLUME', None,
[{'LOWER': 'ml'}],
[{'ORTH': 'dL'}],
[{'LOWER': 'cc'}],
[{'ORTH': 'L'}])
self.time_matcher.add('UNIT_OF_TIME', None,
[{'LOWER': 'sec'}],
[{'LOWER': 'second'}],
[{'LOWER': 'seconds'}],
[{'LOWER': 'min'}],
[{'LOWER': 'minute'}],
[{'LOWER': 'minutes'}],
[{'LOWER': 'hr'}],
[{'LOWER': 'hour'}],
[{'LOWER': 'day'}],
[{'LOWER': 'days'}],
[{'LOWER': 'week'}],
[{'LOWER': 'weeks'}],
[{'LOWER': 'month'}],
[{'LOWER': 'months'}],
[{'LOWER': 'year'}],
[{'LOWER': 'years'}],
[{'LOWER': 'yrs'}]
)
self.frequency_matcher.add('FREQUENCY_MATCHER', None,
[{'LOWER': 'bid'}],
[{'LOWER': 'prn'}],
[{'LOWER': 'qid'}],
[{'LOWER': 'tid'}],
[{'LOWER': 'qd'}],
[{'LOWER': 'daily'}],
[{'LOWER': 'hs'}],
[{'LOWER': 'as'}, {'LOWER': 'needed'}],
[{'LOWER': 'once'}, {'LOWER': 'a'}, {'LOWER': 'day'}],
[{'LOWER': 'twice'}, {'LOWER': 'a'}, {'LOWER': 'day'}]
)
self.form_matcher.add('UNIT_OF_FORM', None,
[{'ORTH': 'dose'}],
[{'ORTH': 'doses'}],
[{'LEMMA': 'pill'}],
[{'LEMMA': 'tablet'}],
[{'LEMMA': 'unit'}],
[{'LEMMA': 'u'}],
[{'LEMMA': 'patch'}],
[{'LEMMA': 'unit'}],
[{'ORTH': 'lotion'}],
[{'ORTH': 'powder'}],
[{'ORTH': 'amps'}],
[{'LOWER': 'actuation'}],
[{'LEMMA': 'suspension'}],
[{'LEMMA': 'syringe'}],
[{'LEMMA': 'puff'}],
[{'LEMMA': 'liquid'}],
[{'LEMMA': 'aerosol'}],
[{'LEMMA': 'cap'}]
)
self.route_matcher.add('TYPE_OF_ROUTE', None,
[{'LOWER': 'IV'}],
[{'ORTH': 'intravenous'}],
[{'LOWER': 'po'}],
[{'ORTH': 'gtt'}],
[{'LOWER': 'drip'}],
[{'LOWER': 'inhalation'}],
[{'LOWER': 'by'}, {'LOWER': 'mouth'}],
[{'LOWER': 'topical'}],
[{'LOWER': 'subcutaneous'}],
[{'LOWER': 'ophthalmic'}],
[{'LEMMA': 'injection'}],
[{'LOWER': 'mucous'}, {'LOWER': 'membrane'}],
[{'LOWER': 'oral'}],
[{'LOWER': 'nebs'}],
[{'LOWER': 'transdermal'}],
[{'LOWER': 'nasal'}]
)
self.unit_of_measurement_matcher.add('UNIT_OF_MEASUREMENT', None,
[{'ENT_TYPE': 'mass_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'volume_unit'}],
[{'ENT_TYPE': 'volume_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'time_unit'}],
[{'ENT_TYPE': 'form_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'volume_unit'}]
)
self.measurement_matcher.add('MEASUREMENT', None,
[{'LIKE_NUM': True}, {'ORTH': '%'}],
[{'LIKE_NUM': True}, {'ENT_TYPE': 'measurement_unit'}],
[{'LIKE_NUM': True}, {'ENT_TYPE': 'mass_unit'}],
[{'LIKE_NUM': True}, {'ENT_TYPE': 'volume_unit'}],
[{'LIKE_NUM': True}, {'ENT_TYPE': 'form_unit'}],
[{'LIKE_NUM': True},{'LOWER': 'x'}, {'ENT_TYPE': 'form_unit'}]
)
self.duration_matcher.add('DURATION', None,
[{'POS': 'PREP'}, {'LIKE_NUM': True}, {'ENT_TYPE': 'time_unit'}],
[{'LIKE_NUM': True}, {'ENT_TYPE': 'time_unit'}],
[{'LOWER': 'in'}, {'LIKE_NUM': True},{'ENT_TYPE': 'time_unit'}],
[{'LOWER': 'prn'}]
)
def __call__(self, doc):
logging.debug("Called UnitAnnotator Component")
nlp = self.nlp
with doc.retokenize() as retokenizer:
#match and tag mass units
matches = self.mass_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['mass_unit'])
if span is None:
raise BaseException("Span is none")
for token in span:
token._.feature_is_mass_unit = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
#match and tag volume units
matches = self.volume_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['volume_unit'])
for token in span:
token._.feature_is_volume_unit = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# match and tag time units
matches = self.time_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['time_unit'])
for token in span:
token._.feature_is_time_unit = True
if len(span) > 1:
retokenizer.merge(span)
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# durations
matches = self.duration_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['duration_pattern'])
for token in span:
token._.feature_is_duration_pattern = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# match and frequency indicators
matches = self.frequency_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['frequency_indicator'])
for token in span:
token._.feature_is_frequency_indicator = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
#match and tag form units
matches = self.form_matcher(doc)
spans = []
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['form_unit'])
for token in span:
token._.feature_is_form_unit = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# match and tag route types
matches = self.route_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['route_type'])
for token in span:
token._.feature_is_route_type = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# match units of measurement (x/y, , etc)
matches = self.unit_of_measurement_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['measurement_unit'])
for token in span:
token._.feature_is_measurement_unit = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
with doc.retokenize() as retokenizer:
# units of measures, numbers , percentages all together
matches = self.measurement_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['measurement'])
for token in span:
token._.feature_is_measurement = True
try:
if len(span) > 1:
retokenizer.merge(span)
except ValueError:
pass
doc.ents = list(doc.ents) + [span]
return doc