def markup_doc(doc_text: str):
context = pyConText.ConTextDocument()
rslts = []
for s in doc_text.split('\.'):
m = markup_sentence(s, modifiers=modifiers, targets=targets)
rslts.append(m)
for r in rslts:
context.addMarkup(r)
return context
def markup_doc(doc_text: str) -> pyConText.ConTextDocument:
rslts = []
context = pyConText.ConTextDocument()
#for s in doc_text.split('.'):
m = markup_sentence(doc_text, modifiers=my_modifiers, targets=my_targets)
rslts.append(m)
for r in rslts:
context.addMarkup(r)
return context
def apply_context(input_context, modifier_path, target_path):
"""
Function that applies context algorithm on patient records input.
Returns dictionary:
{<record number/id>:
{
"object" : <context document>,
"xml" : <context as xml string>
}
}
"""
print("\nNumber of patient records that will be processed:",
len(input_context.index))
# Obtain itemdata
modifiers = itemData.get_items(modifier_path)
targets = itemData.get_items(target_path)
# Initialize context object
context = pyConText.ConTextDocument()
# For each patient record in the input data file:
for record_index in input_context.index:
record_text = input_context.at[record_index, "text"]
record_nr = input_context.at[record_index, "record"]
# check if record is string, otherwise skip markup for record
if isinstance(record_text, str):
# Apply context
context = \
markup_record(context, record_text, record_nr,
modifiers, targets)
else:
print(
f"\nRecord number {record_nr} is no string. This record is skipped."
)
# print(f"Output dict after record: {record_nr}\n{output_dict}")
# n_objects = len(output_dict)
# print(f"\nOutput object contains {n_objects} context objects\n")
return (context)
def analyze_sentence(sentence, modifiers, targets, tagExperiencer=False):
context = pyConText.ConTextDocument()
markup = pyConText.ConTextMarkup()
markup.setRawText(sentence)
markup.markItems(modifiers, mode="modifier")
markup.markItems(targets, mode="target")
markup.pruneMarks()
markup.dropMarks('Exclusion')
markup.applyModifiers()
markup.dropInactiveModifiers()
markup.updateScopes()
context.addMarkup(markup)
g = context.getDocumentGraph()
ma = g.getMarkedTargets()
tst = []
details = []
found = {}
for te in ma:
#print ma
tmp1, tmp2 = getNegationValue(g, te)
if tagExperiencer:
e1, e2 = getExperiencerValue(g, te)
if e1 != 'Other':
tst.append(tmp1)
details.append(tmp2)
found[tmp2] = Counter(tmp1)
else:
tst.append(tmp1)
details.append(tmp2)
found[tmp2] = Counter(tmp1)
return tst, details
markup = pyConText.ConTextMarkup()
markup.setRawText(s)
markup.cleanText()
markup.markItems(modifiers, mode="modifier")
markup.markItems(targets, mode="target")
markup.pruneMarks()
markup.dropMarks('Exclusion')
# apply modifiers to any targets within the modifiers scope
markup.applyModifiers()
markup.pruneSelfModifyingRelationships()
if prune_inactive:
markup.dropInactiveModifiers()
return markup
context = pyConText.ConTextDocument()
# %%
count = 0
rslts = []
for s in sent_tokenize(reports[0]):
m = markup_sentence(s, modifiers=modifiers, targets=targets)
rslts.append(m)
for r in rslts:
context.addMarkup(r)
# %%
clrs = {
"bowel_obstruction": "blue",
"inflammation": "blue",
def markup_context(rslts):
context = pyConText.ConTextDocument()
for r in rslts:
context.addMarkup(r)
return context
def markup_doc(doc_text: str) -> pyConText.ConTextDocument:
rslts = []
context = pyConText.ConTextDocument()