def get_trial_criteria(nct):
""" Returns JSON containing HTML formatted eligibility criteria for one
trial. """
trial = Trial(nct)
trial.load()
return {'criteria': trial.eligibility.formatted_html}
def get_trial_criteria(nct):
""" Returns JSON containing HTML formatted eligibility criteria for one
trial. """
trial = Trial(nct)
trial.load()
return {'criteria': trial.eligibility.formatted_html}
def trials_filter_by(run_id, filter_by):
runner = Runner.get(run_id)
if runner is None:
bottle.abort(404)
if not runner.done:
bottle.abort(400, "Trial results are not available")
ncts = runner.get_ncts(restrict='none')
sess = _get_session()
run_data = sess.get('runs', {}).get(run_id, {})
# demographics - get age and gender
if 'demographics' == filter_by:
f_gender = run_data.get('gender')
f_age = int(run_data.get('age', 0))
for tpl in ncts:
nct = tpl[0]
reason = tpl[1] if len(tpl) > 1 else None
if not reason:
trial = Trial(nct)
trial.load()
# filter gender
if f_gender:
if 'male' == f_gender:
if trial.eligibility.gender == 2:
reason = "Limited to women"
else:
if trial.eligibility.gender == 1:
reason = "Limited to men"
# filter age
if f_age > 0:
if trial.eligibility.min_age and trial.eligibility.min_age > f_age:
reason = "Patient is too young (min age %d)" % trial.eligibility.min_age
elif trial.eligibility.max_age and trial.eligibility.max_age < f_age:
reason = "Patient is too old (max age %d)" % trial.eligibility.max_age
# TODO: REFACTOR into runner class!
if reason:
runner.write_trial_reason(nct, reason)
runner.commit_transactions()
# problems (only if NLP is on)
elif 'problems' == filter_by:
if USE_NLP:
probs = problems().get('problems', [])
# extract snomed codes from patient's problem list
snomed = SNOMEDLookup()
exclusion_codes = []
for problem in probs:
snomed_url = problem.get('sp:problemName',
{}).get('sp:code', {}).get('@id')
if snomed_url is not None:
snomed_code = os.path.basename(snomed_url)
exclusion_codes.append(snomed_code)
# look at trial criteria
for tpl in ncts:
nct = tpl[0]
reason = tpl[1] if len(tpl) > 1 else None
# if we already have a reason, this trial has already been filtered
if not reason:
trial = Trial(nct)
trial.load()
# exclusion criterion matched
if trial.filter_snomed(exclusion_codes) is not None:
reason = 'Matches exclusion criterium "%s" (SNOMED %s)' % (
snomed.lookup_code_meaning(match, True,
True), match)
break
# TODO: REFACTOR
# runner.write_trial_reason(nct, reason)
# unknown filtering property
else:
return '{"error": "We can not filter by %s"}' % filter_by
return '{"status": "ok"}'
def trials_filter_by(run_id, filter_by):
runner = Runner.get(run_id)
if runner is None:
bottle.abort(404)
if not runner.done:
bottle.abort(400, "Trial results are not available")
ncts = runner.get_ncts(restrict='none')
sess = _get_session()
run_data = sess.get('runs', {}).get(run_id, {})
# demographics - get age and gender
if 'demographics' == filter_by:
f_gender = run_data.get('gender')
f_age = int(run_data.get('age', 0))
for tpl in ncts:
nct = tpl[0]
reason = tpl[1] if len(tpl) > 1 else None
if not reason:
trial = Trial(nct)
trial.load()
# filter gender
if f_gender:
if 'male' == f_gender:
if trial.eligibility.gender == 2:
reason = "Limited to women"
else:
if trial.eligibility.gender == 1:
reason = "Limited to men"
# filter age
if f_age > 0:
if trial.eligibility.min_age and trial.eligibility.min_age > f_age:
reason = "Patient is too young (min age %d)" % trial.eligibility.min_age
elif trial.eligibility.max_age and trial.eligibility.max_age < f_age:
reason = "Patient is too old (max age %d)" % trial.eligibility.max_age
# TODO: REFACTOR into runner class!
if reason:
runner.write_trial_reason(nct, reason)
runner.commit_transactions()
# problems (only if NLP is on)
elif 'problems' == filter_by:
if USE_NLP:
probs = problems().get('problems', [])
# extract snomed codes from patient's problem list
snomed = SNOMEDLookup()
exclusion_codes = []
for problem in probs:
snomed_url = problem.get('sp:problemName', {}).get('sp:code', {}).get('@id')
if snomed_url is not None:
snomed_code = os.path.basename(snomed_url)
exclusion_codes.append(snomed_code)
# look at trial criteria
for tpl in ncts:
nct = tpl[0]
reason = tpl[1] if len(tpl) > 1 else None
# if we already have a reason, this trial has already been filtered
if not reason:
trial = Trial(nct)
trial.load()
# exclusion criterion matched
if trial.filter_snomed(exclusion_codes) is not None:
reason = 'Matches exclusion criterium "%s" (SNOMED %s)' % (snomed.lookup_code_meaning(match, True, True), match)
break
# TODO: REFACTOR
# runner.write_trial_reason(nct, reason)
# unknown filtering property
else:
return '{"error": "We can not filter by %s"}' % filter_by
return '{"status": "ok"}'
def trials_json(self, restrict='reason', filter_interventions=None, filter_phases=None):
""" Returns an array of trial JSON for the matching trials, optionally
filtered by intervention type and/or drug phases.
"""
if not self.done:
raise Exception("Trial results are not yet available")
sqlite = SQLite.get(self.sqlite_db)
if sqlite is None:
raise Exception("No SQLite handle, please set up properly")
# look up trials. Currently cheaply filtering by string comparison
qry = "SELECT nct FROM trials WHERE run_id = ? AND reason IS NULL"
if 'reason' == restrict:
qry += ' AND reason IS NULL'
tpls = [self.run_id]
if filter_interventions is not None:
ored = []
for inter in filter_interventions:
ored.append('types LIKE "%%%s%%"' % inter)
# ored.append('instr(types, ?)')
# tpls.append(inter)
if len(ored) > 0:
qry = qry + ' AND (' + ' OR '.join(ored) + ')'
if filter_phases is not None:
ored = []
for phase in filter_phases:
ored.append('phases LIKE "%%%s%%"' % phase)
# ored.append('instr(phases, ?)')
# tpls.append(phase)
if len(ored) > 0:
qry = qry + ' AND (' + ' OR '.join(ored) + ')'
trials = []
fields = ['keyword', 'phase', 'overall_contact']
lat = float(self.reference_location[0]) if self.reference_location else 0
lng = float(self.reference_location[1]) if self.reference_location else 0
# retrieve ncts
qry += ' ORDER BY distance ASC'
for row in sqlite.execute(qry, tuple(tpls)):
trial = Trial(row[0])
trial.load()
trial_dict = trial.json(fields)
# add trial locations
if lat and lng:
closest = []
for loc in trial.locations_closest_to(lat, lng, open_only=True):
closest.append(loc[0].json())
trial_dict['location'] = closest
trials.append(trial_dict)
# grab trial data in batch from db - PROBLEM: distance order is not preserved
# for trial in Trial.retrieve(ncts):
# trials.append(trial.json(fields))
return trials
def trials_json(self,
restrict='reason',
filter_interventions=None,
filter_phases=None):
""" Returns an array of trial JSON for the matching trials, optionally
filtered by intervention type and/or drug phases.
"""
if not self.done:
raise Exception("Trial results are not yet available")
sqlite = SQLite.get(self.sqlite_db)
if sqlite is None:
raise Exception("No SQLite handle, please set up properly")
# look up trials. Currently cheaply filtering by string comparison
qry = "SELECT nct FROM trials WHERE run_id = ? AND reason IS NULL"
if 'reason' == restrict:
qry += ' AND reason IS NULL'
tpls = [self.run_id]
if filter_interventions is not None:
ored = []
for inter in filter_interventions:
ored.append('types LIKE "%%%s%%"' % inter)
# ored.append('instr(types, ?)')
# tpls.append(inter)
if len(ored) > 0:
qry = qry + ' AND (' + ' OR '.join(ored) + ')'
if filter_phases is not None:
ored = []
for phase in filter_phases:
ored.append('phases LIKE "%%%s%%"' % phase)
# ored.append('instr(phases, ?)')
# tpls.append(phase)
if len(ored) > 0:
qry = qry + ' AND (' + ' OR '.join(ored) + ')'
trials = []
fields = ['keyword', 'phase', 'overall_contact']
lat = float(
self.reference_location[0]) if self.reference_location else 0
lng = float(
self.reference_location[1]) if self.reference_location else 0
# retrieve ncts
qry += ' ORDER BY distance ASC'
for row in sqlite.execute(qry, tuple(tpls)):
trial = Trial(row[0])
trial.load()
trial_dict = trial.json(fields)
# add trial locations
if lat and lng:
closest = []
for loc in trial.locations_closest_to(lat, lng,
open_only=True):
closest.append(loc[0].json())
trial_dict['location'] = closest
trials.append(trial_dict)
# grab trial data in batch from db - PROBLEM: distance order is not preserved
# for trial in Trial.retrieve(ncts):
# trials.append(trial.json(fields))
return trials