def build_identifiers(nct_id, study_id, secondary_ids):
"""
Construct the identifiers
:param id_info:
:return:
"""
ident = [
identifier.Identifier(dict(use="usual", value=study_id)),
identifier.Identifier(
dict(use="official",
value=nct_id,
system="https://clinicaltrials.gov/ct2/show/"))
]
for _id in secondary_ids:
ident.append(dict(use='secondary', value=_id))
return ident
def fhir_specimen(obj):
""" Converts biosample to FHIR Specimen. """
specimen = s.Specimen()
specimen.identifier = []
# id
identifier = fhir_indentifier.Identifier()
identifier.value = obj['id']
specimen.identifier.append(identifier)
# individual - subject property in FHIR is mandatory for a specimen
specimen.subject = fhirreference.FHIRReference()
specimen.subject.reference = obj.get('individual', 'unknown')
# sampled_tissue
specimen.type = codeableconcept.CodeableConcept()
specimen.type.coding = []
coding = c.Coding()
coding.code = obj['sampled_tissue']['id']
coding.display = obj['sampled_tissue']['label']
specimen.type.coding.append(coding)
# description
if 'description' in obj.keys():
specimen.note = []
annotation = a.Annotation()
annotation.text = obj.get('description', None)
specimen.note.append(annotation)
# procedure
specimen.collection = s.SpecimenCollection()
specimen.collection.method = fhir_codeable_concept(
obj['procedure']['code'])
if 'body_site' in obj['procedure'].keys():
specimen.collection.bodySite = fhir_codeable_concept(
obj['procedure']['body_site'])
# Note on taxonomy from phenopackets specs:
# Individuals already contain a taxonomy attribute so this attribute is not needed.
# extensions
specimen.extension = []
# individual_age_at_collection
if 'individual_age_at_collection' in obj.keys():
ind_age_at_collection_extension = fhir_age(
obj, PHENOPACKETS_ON_FHIR_MAPPING['biosample']
['individual_age_at_collection'], 'individual_age_at_collection')
specimen.extension.append(ind_age_at_collection_extension)
concept_extensions = codeable_concepts_fields([
'histological_diagnosis', 'tumor_progression', 'tumor_grade',
'diagnostic_markers'
], 'biosample', obj)
for concept in concept_extensions:
specimen.extension.append(concept)
if 'is_control_sample' in obj.keys():
control_extension = extension.Extension()
control_extension.url = PHENOPACKETS_ON_FHIR_MAPPING['biosample'][
'is_control_sample']
control_extension.valueBoolean = obj['is_control_sample']
specimen.extension.append(control_extension)
# TODO 2m extensions - references
return specimen.as_json()
def writeMedProfile(meds_medication,
meds_frequencyPerYear,
meds_fractionOfSubjects,
meds_correlatedLabsCoefficients,
meds_correlatedMedsCoefficients,
meds_correlatedProceduresCoefficients,
meds_correlatedDiagnosisCoefficients,
meds_correlatedPhenotypesCoefficients,
cohort='All',
sex='All',
race='All',
age_low='All',
age_high=None,
topN=10,
correlationCutoff=0.3):
"""Write out Medication Clinical Profile to JSON File and save locally
Keywords:
Structures from output of calculateAnyProfile(profileType='medications')
cohort -- short name for cohort, special characters besides hyphens are prohibited (default 'All')
sex -- specification of whether this is a 'All', 'Male', or 'Female' sex profile (default 'All')
race -- specification of whether this is 'All', 'White or Caucasian', 'Black or African American', 'Other'
race profile (default 'All')
age_low -- low age range for this profile (default 'All')
age_high -- high age range for this profile (default None)
topN -- integer representing the maximum number of correlations to report in the profile, ranked descending (default 10)
correlationCutoff -- minimum correlation coefficient value to report for whole profile (default 0.3)
"""
import os
import sys
import sqlalchemy
import urllib.parse
import pandas as pd
import numpy as np
import getpass
from dataclasses import dataclass
from SciServer import Authentication
from fhirclient.models import clinicalprofile, fhirreference, identifier, codeableconcept, fhirdate, quantity
from datetime import datetime
import json
from fhir_loader import fhir_loader
import pymssql
# Initialize profile
clinicalProfile = clinicalprofile.ClinicalProfile()
if sex == 'M':
sex = 'Male'
elif sex == 'F':
sex = 'Female'
# Header info
if (age_low != 'All'):
clinicalProfile.id = ('jh-medications-' + cohort + '-' + sex + '-' +
race + '-' + str(int(age_low)) + '-' +
str(int(age_high)))
clinicalProfile.identifier = [
identifier.Identifier({
'value': ('jh-medications-' + cohort + '-' + sex + '-' + race +
'-' + str(int(age_low)) + '-' + str(int(age_high)))
})
]
clinicalProfile.cohort = fhirreference.FHIRReference({
'reference':
('Group/jh-medications-' + cohort + '-' + sex + '-' + race + '-' +
str(int(age_low)) + '-' + str(int(age_high)))
})
else:
clinicalProfile.id = 'jh-medications-' + cohort + '-' + sex + '-' + race + '-' + str(
age_low)
clinicalProfile.identifier = [
identifier.Identifier({
'value':
'jh-medications-' + cohort + '-' + sex + '-' + race + '-' +
str(age_low)
})
]
clinicalProfile.cohort = fhirreference.FHIRReference({
'reference': ('Group/jh-medications-' + cohort + '-' + sex + '-' +
race + '-' + str(age_low))
})
clinicalProfile.status = 'draft'
clinicalProfile.population = fhirreference.FHIRReference(
{'reference': 'Group/jh-medications-' + cohort})
clinicalProfile.date = fhirdate.FHIRDate(
str(datetime.now()).replace(' ', 'T'))
clinicalProfile.reporter = fhirreference.FHIRReference({
'reference':
'Organization/JHM',
'type':
'Organization',
'display':
'Johns Hopkins School of Medicine'
})
meds = list()
meds_medication = [x for x in meds_medication if str(x) != 'nan']
for thisMed in meds_medication:
thisCPMed = clinicalprofile.ClinicalProfileMedication()
try:
thisCPMed.medicationCodeableConcept = codeableconcept.CodeableConcept(
dict(coding=[
dict(system='http://www.nlm.nih.gov/research/umls/rxnorm/',
code=str(thisMed))
]))
# meds_dosageInfo
# thisCPMed.dose = quantity.Quantity(dict(unit=str(meds_dosageInfo.loc[thisMed])))
thisCPMed.frequencyPerYear = round(
float(meds_frequencyPerYear.loc[thisMed].mean()), 3)
thisCPMed.fractionOfSubjects = round(
float(meds_fractionOfSubjects.loc[thisMed].mean()), 3)
try:
topNcorrs = (pd.DataFrame(
meds_correlatedLabsCoefficients.loc[thisMed].groupby([
'LAB_LOINC'
]).Relative_Counts.mean()).Relative_Counts.nlargest(
topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherLab = [(dict(
coding=[dict(system='http://loinc.org', code=code)]))]
entries.append(dict(labcode=otherLab, coefficient=corr))
if not entries:
print('No correlated Labs for Med ', thisMed)
else:
thisCPMed.correlatedLabs = clinicalprofile.\
ClinicalProfileLabScalarDistributionCorrelatedLabs(dict(topn=topN, entry=entries))
except:
print('No correlated Labs for Med ', thisMed)
try:
topNcorrs = (pd.DataFrame(
meds_correlatedDiagnosisCoefficients.loc[thisMed].groupby([
'DX'
]).Relative_Counts.mean()).Relative_Counts.nlargest(
topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherDX = (dict(coding=[
dict(system='http://www.icd10data.com/', code=code)
]))
entries.append(dict(code=otherDX, coefficient=corr))
if not entries:
print('No correlated Diagnoses for Med ', thisMed)
else:
thisCPMed.correlatedDiagnoses = clinicalprofile.ClinicalProfileLabScalarDistributionCorrelatedDiagnoses(
dict(topn=topN, entry=entries))
except:
print('No correlated DX for Med ', thisMed)
try:
topNcorrs = (pd.DataFrame(
meds_correlatedProceduresCoefficients.loc[thisMed].groupby(
['RAW_PX'
]).Relative_Counts.mean()).Relative_Counts.nlargest(
topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherProc = [(dict(coding=[
dict(system=
'http://www.ama-assn.org/practice-management/cpt',
code=code)
]))]
entries.append(dict(code=otherProc, coefficient=corr))
if not entries:
print('No correlated Procedures for Med ', thisMed)
else:
thisCPMed.correlatedProcedures = clinicalprofile.ClinicalProfileLabScalarDistributionCorrelatedProcedures(
dict(topn=topN, entry=entries))
except:
print('No correlated Procedures for Med ', thisMed)
try:
topNcorrs = (pd.DataFrame(
meds_correlatedMedsCoefficients.loc[thisMed].groupby([
'JH_INGREDIENT_RXNORM_CODE'
]).Relative_Counts.mean()).Relative_Counts.nlargest(
topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherMed = [
dict(medicationCodeableConcept=dict(coding=[
dict(
system=
'http://www.nlm.nih.gov/research/umls/rxnorm/',
code=code)
]))
]
entries.append(dict(meds=otherMed, coefficient=corr))
if not entries:
print('No correlated Meds for Med ', thisMed)
else:
thisCPMed.correlatedMedications = clinicalprofile.ClinicalProfileLabScalarDistributionCorrelatedMedications(
dict(topn=topN, entry=entries))
except:
print('No correlated Meds for Med ', thisMed)
try:
topNcorrs = (pd.DataFrame(
meds_correlatedPhenotypesCoefficients.loc[thisMed].groupby(
['HPO'
]).Relative_Counts.mean()).Relative_Counts.nlargest(
topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherHPO = (dict(coding=[
dict(system='http://hpo.jax.org/app/', code=code)
]))
entries.append(dict(code=otherHPO, coefficient=corr))
if not entries:
print('No correlated Phenotypes for Med ', thisMed)
else:
thisCPMed.correlatedPhenotypes = clinicalprofile.ClinicalProfileLabScalarDistributionCorrelatedPhenotypes(
dict(topn=topN, entry=entries))
except:
print('No correlated Phenotypes for Med ', thisMed)
meds.append(thisCPMed)
except Exception as e:
print(e)
print('This med did not work ', thisMed)
clinicalProfile.medication = meds
if age_high != None:
filename = cohort + '_resources/jh-medications-' + cohort + '-' + sex + '-' + race + '-' + str(
int(age_low)) + '-' + str(int(age_high)) + '.json'
else:
filename = cohort + '_resources/jh-medications-' + cohort + '-' + sex + '-' + race + '-' + str(
age_low) + '.json'
with open(filename, 'w') as outfile:
json.dump(clinicalProfile.as_json(), outfile, indent=4)
del (clinicalProfile)
return print('Write to ' + filename + ' successful')
limit = 100
for index, row in itertools.islice(df_a.iterrows(), limit):
# for index, row in df_a.iterrows():
patient = p.Patient() # not using rec_id as pandas id, leaving empty
patient.gender = row['sex']
name = hn.HumanName()
name.given = [row['given_name']]
name.family = row['surname']
name.use = 'official'
patient.name = [name]
phone = cp.ContactPoint()
phone.system = 'phone'
phone.value = row['phone_number']
patient.telecom = [phone]
patient.birthDate = fd.FHIRDate(row['date_of_birth'])
emr = ident.Identifier()
emr.system = 'http://clientregistry.org/openmrs'
emr.value = row['rec_id']
art = ident.Identifier()
art.system = 'http://system1/artnumber'
art.value = row['art_number']
nin = ident.Identifier()
nin.system = 'http://system1/nationalid'
nin.value = row['uganda_nin']
patient.identifier = [emr, art, nin]
# print(json.dumps(patient.as_json()))
headers = {'Content-Type': 'application/json'}
start = time.time()
response = post(server,
headers=headers,
def writeLabProfile(labs_counts, labs_frequencyPerYear, labs_fractionOfSubjects,labs_units, labs_names,
labs_stats, labs_aboveBelowNorm, labs_correlatedLabsCoefficients, labs_abscorrelation,
labs_correlatedMedsCoefficients, labs_correlatedProceduresCoefficients,
labs_correlatedDiagnosisCoefficients, labs_correlatedPhenotypesCoefficients,
cohort='All', sex='All', race='All', age_low='All', age_high=None,
topN=10, correlationCutoff=0.3):
"""Write out Lab Clinical Profile to JSON File and save locally
Keywords:
Structures from output of calculateAnyProfile(profileType='labs')
cohort -- short name for cohort, special characters besides hyphens are prohibited (default 'All')
sex -- specification of whether this is a 'All', 'Male', or 'Female' sex profile (default 'All')
race -- specification of whether this is 'All', 'White or Caucasian', 'Black or African American', 'Other' race profile (default 'All')
age_low -- low age range for this profile (default 'All')
age_high -- high age range for this profile (default None)
topN -- integer representing the maximum number of correlations to report in the profile, ranked descending (default 10)
correlationCutoff -- minimum correlation coefficient value to report for whole profile (default 0.3)
"""
import os
import sys
import sqlalchemy
import urllib.parse
import pandas as pd
import numpy as np
import getpass
from dataclasses import dataclass
from SciServer import Authentication
from datetime import datetime
import json
from fhir_loader import fhir_loader
from fhirclient.models import clinicalprofile, fhirreference, identifier, codeableconcept, fhirdate, quantity
import pymssql
# Initialize profile
clinicalProfile = clinicalprofile.ClinicalProfile()
clinicalProfile.resourceType = 'ClinicalProfile'
if sex == 'M':
sex = 'Male'
elif sex =='F':
sex = 'Female'
# Header info
if (age_low != 'All'):
clinicalProfile.id = 'jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(int(age_low))+'-'+str(int(age_high))
clinicalProfile.identifier = [identifier.Identifier({'value':
'jh-labs-'+cohort+'-'+sex+'-'+race+'-'+
str(int(age_low))+'-'+str(int(age_high))})]
clinicalProfile.cohort = fhirreference.FHIRReference({'reference':
'Group/jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(int(age_low))
+'-'+str(int(age_high))})
else:
clinicalProfile.id = 'jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(age_low)
clinicalProfile.identifier = [identifier.Identifier({'value':
'jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(age_low)})]
clinicalProfile.cohort = fhirreference.FHIRReference({'reference':
'Group/jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(age_low)})
clinicalProfile.status = 'draft'
clinicalProfile.population = fhirreference.FHIRReference({'reference': 'Group/jh-labs-'+cohort})
clinicalProfile.date = fhirdate.FHIRDate(str(datetime.now()).replace(' ', 'T'))
clinicalProfile.reporter = fhirreference.FHIRReference({'reference': 'Organization/JHM',
'type': 'Organization',
'display': 'Johns Hopkins School of Medicine'})
## LABS
labs = list()
corrmat = (pd.DataFrame(labs_correlatedLabsCoefficients).unstack(level=[0,1]).corr(min_periods=50)
.droplevel(level=0).droplevel(level=0,axis=1))
lab_names = pd.DataFrame({'lab_name':labs_names}).reset_index()
lab_counts = pd.DataFrame({'lab_counts':labs_counts}).reset_index().rename({'index':'LAB_LOINC'},axis=1)
lab_info = lab_names.merge(lab_counts, how='inner', on='LAB_LOINC').set_index('LAB_LOINC')
for thisLab in lab_info.index:
# Check if STDEV is NaN and skip that lab if so
if np.isnan(float(labs_stats.loc[thisLab]['std'].median())):
continue
# Build the profile
thisCPLab = clinicalprofile.ClinicalProfileLab()
# try:
thisCPLab.code = [codeableconcept.CodeableConcept(dict(coding=[dict(system='http://loinc.org',
code=thisLab)],
text=lab_info.loc[thisLab]['lab_name'][0]))]
thisCPLab.count = int(lab_info.loc[thisLab]['lab_counts'])
thisCPLab.frequencyPerYear = round(float(labs_frequencyPerYear.loc[thisLab].mean()),3)
thisCPLab.fractionOfSubjects = round(float(labs_fractionOfSubjects.loc[thisLab].mean()),3)
thisCPLab.scalarDistribution = clinicalprofile.ClinicalProfileLabScalarDistribution()
thisCPLab.scalarDistribution.units = quantity.Quantity(dict(unit=str(labs_units.loc[thisLab][0])))
thisCPLab.scalarDistribution.min = round(float(labs_stats.loc[thisLab]['min'].min()),3)
thisCPLab.scalarDistribution.max = round(float(labs_stats.loc[thisLab]['max'].max()),3)
thisCPLab.scalarDistribution.mean = round(float(labs_stats.loc[thisLab]['mean'].mean()),3)
thisCPLab.scalarDistribution.median = round(float(labs_stats.loc[thisLab]['median'].median()),3)
thisCPLab.scalarDistribution.stdDev = round(float(labs_stats.loc[thisLab]['std'].median()),3)
deciles = list()
for dec in labs_stats.columns[5:]:
deciles.append(clinicalprofile.ClinicalProfileLabScalarDistributionDecile(
dict(nth=int(dec),
value=round(labs_stats.loc[thisLab][dec].mean(),3))))
thisCPLab.scalarDistribution.decile = deciles
thisCPLab.scalarDistribution.fractionAboveNormal = round(float(labs_aboveBelowNorm.loc[thisLab].aboveNorm.mean()),3)
thisCPLab.scalarDistribution.fractionBelowNormal = round(float(labs_aboveBelowNorm.loc[thisLab].belowNorm.mean()),3)
try:
yearly_vals = dict()
for year in corrmat.loc[thisLab].index:
crosstab = corrmat.loc[(thisLab, year)]
yearly_vals[year] = (crosstab[crosstab.index.get_level_values(level=1).astype('float') == year]
.droplevel(level=1))
topNcorrs = pd.DataFrame(yearly_vals).apply(np.mean, axis=1).drop(thisLab).nlargest(topN).round(3)
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherLoinc = [(dict(coding=[dict(system='http://loinc.org', code=code)],
text=str(lab_info.loc[code]['lab_name'][0])))]
entries.append(dict(labcode=otherLoinc, coefficient=corr))
if not entries:
print('No correlated Labs for Lab ', thisLab)
else:
thisCPLab.scalarDistribution.correlatedLabs = clinicalprofile.ClinicalProfileLabScalarDistributionCorrelatedLabs(
dict(topn=topN,
entry=entries))
except:
print('No correlated Labs for Lab ', thisLab)
try:
topNcorrs = (pd.DataFrame(labs_correlatedMedsCoefficients.loc[thisLab].groupby(['JH_INGREDIENT_RXNORM_CODE'])
.Relative_Counts.mean())
.Relative_Counts.nlargest(topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherRX = [dict(medicationCodeableConcept=dict(coding=
[dict(system='http://www.nlm.nih.gov/research/umls/rxnorm/', code=code)]))]
entries.append(dict(meds=otherRX, coefficient=corr))
if not entries:
print('No correlated Meds for Lab ', thisLab)
else:
thisCPLab.scalarDistribution.correlatedMedications = clinicalprofile.\
ClinicalProfileLabScalarDistributionCorrelatedMedications(
dict(topn=topN,
entry=entries))
except:
print('No correlated Meds for Lab ', thisLab)
try:
topNcorrs = (pd.DataFrame(labs_correlatedDiagnosisCoefficients.loc[thisLab].groupby(['DX'])
.Relative_Counts.mean())
.Relative_Counts.nlargest(topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherDX = (dict(coding=[dict(system='http://www.icd10data.com/', code=code)]))
entries.append(dict(code=otherDX, coefficient=corr))
if not entries:
print('No correlated Diagnoses for Lab ', thisLab)
else:
thisCPLab.scalarDistribution.correlatedDiagnoses = clinicalprofile.\
ClinicalProfileLabScalarDistributionCorrelatedDiagnoses(
dict(topn=topN,
entry=entries))
except:
print('No correlated Diagnoses for Lab ', thisLab)
try:
topNcorrs = (pd.DataFrame(labs_correlatedProceduresCoefficients.loc[thisLab].groupby(['RAW_PX'])
.Relative_Counts.mean())
.Relative_Counts.nlargest(topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherProc = [(dict(coding=[dict(system='http://www.ama-assn.org/practice-management/cpt', code=code)]))]
entries.append(dict(code=otherProc, coefficient=corr))
if not entries:
print('No correlated Procedures for Lab ', thisLab)
else:
thisCPLab.scalarDistribution.correlatedProcedures = clinicalprofile.\
ClinicalProfileLabScalarDistributionCorrelatedProcedures(
dict(topn=topN,
entry=entries))
except:
print('No correlated Procedures for Lab ', thisLab)
try:
topNcorrs = (pd.DataFrame(labs_correlatedPhenotypesCoefficients.loc[thisLab].groupby(['HPO'])
.Relative_Counts.mean())
.Relative_Counts.nlargest(topN).round(3))
entries = list()
for code, corr in topNcorrs.iteritems():
if corr <= correlationCutoff:
continue
otherHPO = (dict(coding=[dict(system='http://hpo.jax.org/app/', code=code)]))
entries.append(dict(code=otherHPO, coefficient=corr))
if not entries:
print('No correlated Phenotypes for Lab ', thisLab)
else:
thisCPLab.scalarDistribution.correlatedPhenotypes = clinicalprofile.\
ClinicalProfileLabScalarDistributionCorrelatedPhenotypes(
dict(topn=topN,
entry=entries))
except:
print('No correlated Phenotypes for Lab ', thisLab)
labs.append(thisCPLab)
# except:
# print('This lab did not work ', thisLab)
clinicalProfile.lab = labs
if age_high != None:
filename = cohort+'_resources/jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(int(age_low))+'-'+str(int(age_high))+'.json'
else:
filename = cohort+'_resources/jh-labs-'+cohort+'-'+sex+'-'+race+'-'+str(age_low)+'.json'
with open(filename, 'w') as outfile:
json.dump(clinicalProfile.as_json(), outfile, indent=4)
del(clinicalProfile)
return print('Write to '+ filename + ' successful')
def identifier(self):
return idn.Identifier({
"use": "official",
"system": "http://hl7.org/fhir/sid/us-ssn",
"value": "xxxxx"
})
return 'draft'
if __name__ == "__main__":
ct = ClinicalStudy.from_file("examples/NCT02348489.xml", local_schema=True)
locations = [build_location(x) for x in ct.locations]
foci = build_focus(ct.mesh_terms)
print([x.as_json() for x in foci])
keywords = build_keyword(ct.keywords)
print([x.as_json() for x in keywords])
identifiers = build_identifiers(ct.nct_id, ct.study_id, ct.secondary_id)
print([x.as_json() for x in identifiers])
package = bundle.Bundle()
package.type = "document"
package.identifier = identifier.Identifier(
dict(use='official',
system="https://clinicaltrials.gov/ct2/show/",
value=ct.nct_id))
entries = []
for location in locations:
be = bundle.BundleEntry()
be.resource_type = "Location"
be.resource = location
entries.append(be)
study = researchstudy.ResearchStudy()
study.identifier = identifiers
print("Study Identifier: ", type(study.identifier[0]))
study.period = period.Period(
dict(start=ct.trail.study_first_posted.date.strftime("%Y-%m-%d")))
study.status = build_status(ct.status)
entries.append(study)
package.entry = entries
def create_fhir_object(self):
"""
Generate a fhirclient.Patient class object and store in the protected attribute _fhir
:return:
None
"""
# Patient object must be persistent to generate FHIR attributes
ins = inspect(self)
if ins.persistent:
# Initialize Patient resource
fhir_pt = fhir_patient.Patient()
# Set resource logical identifier
fhir_pt.id = self.get_url()
# Build and assign Meta resource for Patient object
fhir_meta = meta.Meta()
fhir_meta.lastUpdated = fhir_gen_datetime(value=self.updated_at, error_out=False, to_date=False)
fhir_meta.versionId = str(self.version_number)
fhir_meta.profile = ['http://hl7.org/fhir/us/core/StructureDefinition/us-core-patient']
fhir_pt.meta = fhir_meta
# Patient name represented as HumanName resource
fhir_pt.name = []
fhir_pt.name.append(fhir_gen_humanname(use='usual', first_name=self.first_name, last_name=self.last_name,
middle_name=self.middle_name, suffix=self.suffix,
prefix=self.prefix))
# Display MRN as identifier codeable concept = Patient.identifier.codeableconcept.coding
# Initialize Identifier resource
id_mrn = identifier.Identifier()
id_mrn.use = 'usual'
id_mrn.system = 'http://unkani.com'
id_mrn.value = str(self.uuid)
# Initialize CodeableConcept resource
mrn_cc = codeableconcept.CodeableConcept()
mrn_cc.text = 'Medical Record Number'
# Initialize Coding resource
mrn_coding = coding.Coding()
mrn_coding.system = 'http://hl7.org/fhir/v2/0203'
mrn_coding.code = 'MR'
mrn_coding.display = 'Medical Record Number'
# Assign Coding resource to CodeableConcept
mrn_cc.coding = [mrn_coding]
# Assign CodeableConcept to Identifier
id_mrn.type = mrn_cc
# Assign CodeableConcept to Patient
fhir_pt.identifier = [id_mrn]
# Display SSN as identifier codeable concept = Patient.identifier.codeableconcept.coding
if self.ssn:
# Initialize Identifier resource
id_ssn = identifier.Identifier()
id_ssn.use = 'usual'
id_ssn.system = 'http://hl7.org/fhir/sid/us-ssn'
id_ssn.value = self.ssn
# Initialize CodeableConcept resource
ssn_cc = codeableconcept.CodeableConcept()
ssn_cc.text = 'Social Security Number'
# Initialize Coding resource
ssn_coding = coding.Coding()
ssn_coding.system = 'http://hl7.org/fhir/v2/0203'
ssn_coding.code = 'SS'
ssn_coding.display = 'Social Security Number'
# Assign Coding resource to CodeableConcept
ssn_cc.coding = [ssn_coding]
# Assign CodeableConcept to Identifier
id_ssn.type = ssn_cc
# Assign CodeableConcept to Patient
fhir_pt.identifier.append(id_ssn)
if self.marital_status:
marital_status_cc = codeableconcept.CodeableConcept()
marital_status_url = 'http://hl7.org/fhir/ValueSet/marital-status'
marital_status_concept = ValueSet.get_valueset_concept(marital_status_url, self.marital_status)
if marital_status_concept:
marital_status_cc.text = getattr(marital_status_concept, 'display')
marital_status_coding = coding.Coding()
marital_status_coding.code = self.marital_status
marital_status_coding.system = marital_status_url
marital_status_coding.display = marital_status_cc.text
marital_status_cc.coding = [marital_status_coding]
fhir_pt.maritalStatus = marital_status_cc
if self.race:
ext_race = extension.Extension()
ext_race.url = 'http://hl7.org/fhir/StructureDefinition/us-core-race'
race_url = 'http://hl7.org/fhir/us/core/ValueSet/omb-race-category'
cc_race = codeableconcept.CodeableConcept()
race_concept = ValueSet.get_valueset_concept(race_url, self.race)
if race_concept:
cc_race.text = getattr(race_concept, 'display')
coding_race = coding.Coding()
coding_race.system = race_url
coding_race.code = self.race
coding_race.display = cc_race.text
cc_race.coding = [coding_race]
ext_race.valueCodeableConcept = cc_race
try:
fhir_pt.extension.append(ext_race)
except AttributeError:
fhir_pt.extension = [ext_race]
if self.ethnicity:
ext_ethnicity = extension.Extension()
ext_ethnicity.url = 'http://hl7.org/fhir/us/core/StructureDefinition/us-core-ethnicity'
cc_ethnicity = codeableconcept.CodeableConcept()
cc_ethnicity.text = ethnicity_dict.get(self.ethnicity)[0].capitalize()
coding_ethnicity = coding.Coding()
coding_ethnicity.system = 'http://hl7.org/fhir/us/core/ValueSet/omb-ethnicity-category'
coding_ethnicity.code = self.race
coding_ethnicity.display = cc_ethnicity.text
cc_ethnicity.coding = [coding_ethnicity]
ext_ethnicity.valueCodeableConcept = cc_ethnicity
try:
fhir_pt.extension.append(ext_ethnicity)
except AttributeError:
fhir_pt.extension = [ext_ethnicity]
if self.sex:
sex_dict = {"administrativeGender": {"M": "male", "F": "female", "u": "unknown", "o": "other"},
"usCoreBirthSex": {"M": "M", "F": "F", "U": "UNK", "O": "UNK"}}
fhir_pt.gender = sex_dict['administrativeGender'][str(self.sex).upper()]
ext_birth_sex = extension.Extension()
ext_birth_sex.url = 'http://hl7.org/fhir/us/core/StructureDefinition/us-core-birthsex'
ext_birth_sex.valueCode = sex_dict['usCoreBirthSex'][str(self.sex).upper()]
try:
fhir_pt.extension.append(ext_birth_sex)
except AttributeError:
fhir_pt.extension = [ext_birth_sex]
if self.dob:
fhir_pt.birthDate = fhir_gen_datetime(value=self.dob, to_date=True)
fhir_pt.active = self.active
fhir_pt.deceasedBoolean = self.deceased
if self.deceased_date:
fhir_pt.deceasedDateTime = fhir_gen_datetime(value=self.deceased_date, to_date=False)
if self.preferred_language:
fhir_comm = fhir_patient.PatientCommunication()
fhir_comm.preferred = True
fhir_lang_cc = codeableconcept.CodeableConcept()
fhir_lang_coding = coding.Coding()
fhir_lang_coding.code = self.preferred_language
fhir_lang_url = 'http://hl7.org/fhir/ValueSet/languages'
fhir_lang_coding.system = fhir_lang_url
fhir_lang_concept = ValueSet.get_valueset_concept(fhir_lang_url, self.preferred_language)
if fhir_lang_concept:
fhir_lang_coding.display = fhir_lang_concept.display
fhir_lang_cc.text = fhir_lang_coding.display
fhir_lang_cc.coding = [fhir_lang_coding]
fhir_comm.language = fhir_lang_cc
fhir_pt.communication = [fhir_comm]
contact_point_list = []
phone_list = self.phone_numbers.all()
if phone_list:
for ph in phone_list:
contact_point_list.append(ph.fhir)
email_list = self.email_addresses.all()
if email_list:
for em in email_list:
contact_point_list.append(em.fhir)
if contact_point_list:
fhir_pt.telecom = contact_point_list
address_list = self.addresses.all()
if address_list:
fhir_pt.address = []
for addr in address_list:
fhir_pt.address.append(addr.fhir)
xhtml = render_template('fhir/patient.html', fhir_patient=fhir_pt, patient=self)
fhir_pt.text = narrative.Narrative()
fhir_pt.text.status = 'generated'
fhir_pt.text.div = xhtml
self._fhir = fhir_pt
def createPatient(dohmpifield, record):
#extract fields from dohmpi from database
field = []
for i in range(len(dohmpifield)):
f1, = dohmpifield[i] # dohmpi loads from db as a list of onetuples.
field.append(f1) # we make a traditional list to refer to.
fnamei = field.index('provider_first')
lnamei = field.index('provider_last')
genderi = field.index('provider_gender_cd')
deathyi = field.index('deathccyy')
deathmi = field.index('deathmm')
deathdi = field.index('deathdd')
birthi = field.index('provider_dob_dob')
provi = field.index('provider_license_no')
# record is one record from the postgres db
import fhirclient.models.patient as p
# set patient id as provider licence numberS
pid = record[provi]
p = p.Patient({})
import fhirclient.models.identifier as idf
identifier = idf.Identifier()
identifier.value = pid
p.identifier = [identifier]
# resolve name
import fhirclient.models.humanname as hn
name = hn.HumanName()
name.given = [record[fnamei]]
name.family = [record[lnamei]]
p.name = [name]
#[name.family, name.given]
# resolve gender
if (record[genderi] == "1"):
p.gender = 'male'
elif (record[genderi] == "2"):
p.gender = 'female'
else:
p.gender = 'unknown'
# build and resolve birthdate
birthdate = record[birthi]
bsep1 = birthdate.index("/")
bsep2 = birthdate.index("/", bsep1 + 1)
year = birthdate[-4:]
month = birthdate[0:bsep1]
if (len(month) < 2):
month = "0%s" % month
date = birthdate[bsep1 + 1:bsep2]
if (len(date) < 2):
date = "0%s" % date
birthdate = year + "-" + month + "-" + date
p.birthDate = fhirclient.models.fhirdate.FHIRDate(birthdate)
# build and resolve death date
tt = "12:00:00" #Setting time to noon
yyyy = record[deathyi]
mm = record[deathmi]
if (len(mm) < 2):
mm = "0%s" % mm
dd = record[deathdi]
if (len(dd) < 2):
dd = "0%s" % dd
deathdate = "%s-%s-%sT%s" % (yyyy, mm, dd, tt)
deathdate = fhirclient.models.fhirdate.FHIRDate(deathdate)
# Create period
import fhirclient.models.period as per
per = per.Period()
per.start = deathdate
p.deceasedBoolean = True
p.deceasedDateTime = deathdate
return p
def biosample_to_fhir(obj):
"""Converts Biosample to FHIR Specimen.
:param obj: Biosample json
:return: FHIR Specimen json
"""
schema_path = os.path.join(SCHEMA_PATH, 'biosample_schema.json')
try:
validate_schema(schema_path, obj)
except jsonschema.exceptions.ValidationError:
raise Exception("The biosample object is not valid.")
specimen = s.Specimen()
specimen.identifier = []
# id
identifier = fhir_indentifier.Identifier()
identifier.value = obj['id']
specimen.identifier.append(identifier)
# individual - subject property in FHIR is mandatory for a specimen
specimen.subject = fhirreference.FHIRReference()
specimen.subject.reference = obj.get('individual', 'unknown')
# sampled_tissue
specimen.type = codeableconcept.CodeableConcept()
specimen.type.coding = []
coding = c.Coding()
coding.code = obj['sampledTissue']['id']
coding.display = obj['sampledTissue']['label']
specimen.type.coding.append(coding)
# description
if 'description' in obj:
specimen.note = []
annotation = a.Annotation()
annotation.text = obj.get('description', None)
specimen.note.append(annotation)
# procedure
specimen.collection = s.SpecimenCollection()
specimen.collection.method = fhir_codeable_concept(
obj['procedure']['code'])
if 'bodySite' in obj['procedure']:
specimen.collection.bodySite = fhir_codeable_concept(
obj['procedure']['bodySite'])
# Note on taxonomy from phenopackets specs:
# Individuals already contain a taxonomy attribute so this attribute is not needed.
# extensions
specimen.extension = []
# individual_age_at_collection
if 'individualAgeAtCollection' in obj:
ind_age_at_collection_extension = age_to_fhir(
obj, PHENOPACKETS_ON_FHIR_MAPPING['biosample']
['individualAgeAtCollection'], 'individualAgeAtCollection')
specimen.extension.append(ind_age_at_collection_extension)
concept_extensions = codeable_concepts_fields([
'histologicalDiagnosis', 'tumorProgression', 'tumorGrade',
'diagnosticMarkers'
], 'biosample', obj)
for concept in concept_extensions:
specimen.extension.append(concept)
if 'isControlSample' in obj:
control_extension = extension.Extension()
control_extension.url = PHENOPACKETS_ON_FHIR_MAPPING['biosample'][
'isControlSample']
control_extension.valueBoolean = obj['isControlSample']
specimen.extension.append(control_extension)
# TODO 2m extensions - references
return specimen.as_json()