def createTimingResource(self):
from fhirclient.models import timing, fhirdate
timing_resource = timing.Timing()
# once(frequency) per week(period/periodUnit)
repeat = timing.TimingRepeat()
repeat.frequency = int(self.frequency.value)
repeat.periodUnit = self.frequency.unit
repeat.period = float(self.frequency.unit_period)
# Start Date
start_fhirdate = fhirdate.FHIRDate(self.period.start)
end_fhirdate = fhirdate.FHIRDate(self.period.end)
print(start_fhirdate)
print(end_fhirdate)
print(fhirdate.FHIRDate(self.period.start))
from fhirclient.models import period
fhir_period = period.Period()
fhir_period.start = start_fhirdate
fhir_period.end = end_fhirdate
repeat.boundsPeriod = fhir_period
timing_resource.repeat = repeat
return timing_resource
def get_responseperiod_x(start,end):
responseperiod_x = Ext.Extension()
responseperiod_x.url = 'http://fhir.org/guides/argonaut-questionnaire/StructureDefinition/extension-responsePeriod'
responseperiod_x.valuePeriod = P.Period()
responseperiod_x.valuePeriod.start = FD.FHIRDate(start)
responseperiod_x.valuePeriod.end = FD.FHIRDate(end)
return responseperiod_x
def to_fhir_obj(self):
observation = o.Observation(base_observation)
#observation.subject_reference = self.subject_reference
observation.subject.reference = self.subject_reference
observation.performer[0].reference = self.subject_reference
observation.effectiveDateTime = fd.FHIRDate(str(datetime.utcnow().isoformat()))
observation.issued = fd.FHIRDate(str(datetime.utcnow().isoformat()))
observation.valueString = self.sexual_orientation
return observation
def fhir_composition(obj):
""" Converts Phenopacket to FHIR Composition. """
composition = comp.Composition()
composition.id = obj['id']
composition.subject = fhirreference.FHIRReference()
composition.subject.reference = str(obj['subject']['id'])
composition.title = PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['title']
# elements in Composition required by FHIR spec
composition.status = 'preliminary'
composition.author = []
author = fhirreference.FHIRReference()
author.reference = obj['meta_data']['created_by']
composition.author.append(author)
composition.date = fhirdate.FHIRDate(obj['meta_data']['created'])
composition.type = fhir_codeable_concept({
"id":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['code']['code'],
"label":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['title'],
"system":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['code']['system']
})
composition.section = []
sections = ['biosamples', 'variants', 'diseases', 'hts_files']
for section in sections:
if obj[section]:
section_content = _get_section_object(obj.get(section, None),
section)
composition.section.append(section_content)
return composition.as_json()
def patient_list():
start = request.args.get('start', 0)
length = request.args.get('length', 100)
start = int(start)
length = int(length)
count = db.engine.execute("select count(*) from T_Patients").scalar()
result = db.engine.execute("select * from T_Patients limit %d, %d" %
(start, length))
plist = []
total = count
for row in result:
patient = p.Patient({'id': row['pat_id']})
name = hn.HumanName()
name.given = [row['pat_first']]
name.family = row['pat_last']
patient.name = [name]
address = addr.Address()
address.city = row['pat_city']
address.state = row['pat_state']
address.postalCode = row['pat_zip']
address.district = row['pat_address']
patient.address = [address]
patient.gender = row['pat_gender']
patient.birthDate = fd.FHIRDate(
datetime.strftime(row['pat_birthdate'], "%Y-%m-%d"))
# patient.maritalStatus = row['pat_marital']
plist.append(patient.as_json())
return jsonify(results=plist, total=total)
def hts_file_to_fhir(obj):
"""Converts HTS file to FHIR DocumentReference.
:param obj: HTS file json
:return: FHIR DocumentReference json
"""
schema_path = os.path.join(SCHEMA_PATH, 'hts_file_schema.json')
try:
validate_schema(schema_path, obj)
except jsonschema.exceptions.ValidationError:
raise Exception("The hts file object is not valid.")
doc_ref = documentreference.DocumentReference()
doc_ref.type = fhir_codeable_concept({
"label": obj['htsFormat'],
"id": obj['htsFormat']
})
# GA4GH requires status with the fixed value
doc_ref.status = PHENOPACKETS_ON_FHIR_MAPPING['htsFile']['status']
doc_ref.content = []
doc_content = documentreference.DocumentReferenceContent()
doc_content.attachment = attachment.Attachment()
doc_content.attachment.url = obj['uri']
if 'description' in obj:
doc_content.attachment.title = obj['description']
doc_ref.content.append(doc_content)
doc_ref.indexed = fhirdate.FHIRDate()
doc_ref.indexed.date = datetime.now()
doc_ref.extension = []
genome_assembly = extension.Extension()
genome_assembly.url = PHENOPACKETS_ON_FHIR_MAPPING['htsFile'][
'genomeAssembly']
genome_assembly.valueString = obj['genomeAssembly']
doc_ref.extension.append(genome_assembly)
return doc_ref.as_json()
def create_sleep_observations(patient_ref):
form_data = repo.generate_patient_sleep_data()
for measurement in form_data.items(): # (date, val)
date = measurement[0]
value = measurement[1]
observation = obs.Observation()
observation.status = "registered" # <code>
ref_patient = fhirref.FHIRReference()
ref_patient.reference = "Patient/" + str(patient_ref)
observation.subject = ref_patient
observation.code = models.codeableconcept.CodeableConcept()
code = models.coding.Coding()
code.system = "SNOMED CT"
code.code = "248263006"
code.display = "Sleep Duration"
observation.code.coding = [code]
quantity = models.quantity.Quantity()
quantity.value = value
quantity.unit = "h"
quantity.system = "http://unitsofmeasure.org"
quantity.code = "h"
observation.valueQuantity = quantity
observation.effectiveDateTime = fhirdate.FHIRDate(date)
result = observation.create(smart.server)
print(result)
def createobservation(cls,
valueString=str,
code=None,
codesystem=None,
codedisplay=None,
date=None):
from . import fhirutils
obs = observation.Observation()
# Date
fdate = fhirdate.FHIRDate()
fdate.date = date or datetime.datetime.utcnow()
obs.effectiveDateTime = fdate
# Ontology
if code is not None and codesystem is not None:
obs.code = fhirutils.FHIRUtils.codeableconcept(
code, codesystem, codedisplay)
# status
obs.status = 'final'
# Value
obs.valueString = valueString or None
return obs
def patient(id):
# https://{FHIR Base URL}/Patient/{id}?
# name={name}&
# family={family name}&
# gender={gender}&
# _format=json
# TODO: query params
name = request.args.get('name', None)
family = request.args.get('family', None)
gender = request.args.get('gender', None)
# TODO: connect to database to search patient
result = db.engine.execute("select * from T_Patients limit 1")
names = []
for row in result:
patient = p.Patient({'id': row['pat_id']})
name = hn.HumanName()
name.given = [row['pat_first']]
name.family = row['pat_last']
patient.name = [name]
address = addr.Address()
address.city = row['pat_city']
address.state = row['pat_state']
address.postalCode = row['pat_zip']
address.district = row['pat_address']
patient.address = [address]
patient.gender = row['pat_gender']
patient.birthDate = fd.FHIRDate(
datetime.strftime(row['pat_birthdate'], "%Y-%m-%d"))
patient.maritalStatus = row['pat_marital']
return jsonify(result=patient.as_json())
def create_QR_for_single_patient(patient_ref, form_data, name):
for measurement in form_data.items():
date = measurement[0]
categories = measurement[1]
qr = qresponse.QuestionnaireResponse()
qr.status = "completed"
qr.authored = fhirdate.FHIRDate(date)
qr.questionnaire = models.fhirreference.FHIRReference()
qr.questionnaire.reference = getQuestionnaire(name)
ref_patient = fhirref.FHIRReference()
ref_patient.reference = patient_ref
qr.subject = ref_patient
answer_set = []
for i, (key, val) in enumerate(categories.items()):
qr_item = qresponse.QuestionnaireResponseItem()
qr_item.text = key
qr_item.answer = [qresponse.QuestionnaireResponseItemAnswer()]
qr_item.answer[0].valueInteger = int(val)
qr_item.linkId = str(
i) # Should correspond to item from Questionnaire
answer_set.append(qr_item)
qr.item = answer_set
result = qr.create(smart.server)
print(result)
def fhir_document_reference(obj):
""" Converts HTS file to FHIR DocumentReference. """
doc_ref = documentreference.DocumentReference()
doc_ref.type = fhir_codeable_concept({
"label": obj['hts_format'],
"id": obj['hts_format']
})
# GA4GH requires status with the fixed value
doc_ref.status = PHENOPACKETS_ON_FHIR_MAPPING['hts_file']['status']
doc_ref.content = []
doc_content = documentreference.DocumentReferenceContent()
doc_content.attachment = attachment.Attachment()
doc_content.attachment.url = obj['uri']
if 'description' in obj.keys():
doc_content.attachment.title = obj.get('description', None)
doc_ref.content.append(doc_content)
doc_ref.indexed = fhirdate.FHIRDate()
# check what date it should be - when it's retrieved or created
doc_ref.indexed.date = datetime.now()
doc_ref.extension = []
genome_assembly = extension.Extension()
genome_assembly.url = PHENOPACKETS_ON_FHIR_MAPPING['hts_file'][
'genome_assembly']
genome_assembly.valueString = obj['genome_assembly']
doc_ref.extension.append(genome_assembly)
return doc_ref.as_json()
def createRequestResource(self,
server=FHIRServer,
patient=None,
practitioner=None,
schedule=None):
from . import fhirutils
if self.instrument is None:
print("missin instrument to request")
return None
# Take instrument or from the argument
instr = self.instrument
# create new `ServiceRequest`
serviceRequest = servicerequest.ServiceRequest()
serviceRequest.status = 'active'
serviceRequest.intent = 'plan'
serviceRequest.category = self.category or [
PRORequest.EvaluationCodeableConcept()
]
# Coding Request
if instr.code is not None:
serviceRequest.code = fhirutils.FHIRUtils.codeableconcept(
instr.code, instr.codesystem, instr.title)
# Tag Patient
if patient is not None:
patientreference = fhirreference.FHIRReference()
patientreference.reference = f'Patient/{patient.id}'
serviceRequest.subject = patientreference
# Tag Practitioner
if practitioner is not None:
practitionerreference = fhirreference.FHIRReference()
practitionerreference.reference = f'Practitioner/{practitioner.id}'
practitionerreference.display = practitioner.name[0].text
serviceRequest.requester = practitionerreference
# Schedule
if schedule is not None:
timing_resource = schedule.createTimingResource()
serviceRequest.occurrenceTiming = timing_resource
else:
now = datetime.datetime.utcnow()
fhirDate = fhirdate.FHIRDate()
fhirDate.date = now
serviceRequest.occurrenceDateTime = fhirDate
# Questionnaire Extension if Instrument is a `Questionnaire`
if self.is_questionnaire():
extension = PRORequest.QuestionnaireExtension(
questionnaire=self.instrument.resource)
serviceRequest.extension = [extension]
return serviceRequest
def individual_to_fhir(obj):
"""Converts Individual to FHIR Patient.
:param obj: Individual json
:return: FHIR Patient json
"""
# first validate if phenopackets object is well-formed
schema_path = os.path.join(SCHEMA_PATH, 'individual_schema.json')
try:
validate_schema(schema_path, obj)
except jsonschema.exceptions.ValidationError:
raise Exception("The individual object is not valid.")
patient = p.Patient()
patient.id = obj['id']
patient.birthDate = fhirdate.FHIRDate(obj.get('dateOfBirth', None))
patient.gender = obj.get('sex', None)
patient.active = obj.get('active', None)
patient.deceasedBoolean = obj.get('deceased', None)
patient.extension = list()
# age
if 'age' in obj:
age_extension = age_to_fhir(
obj, PHENOPACKETS_ON_FHIR_MAPPING['individual']['age'], 'age')
patient.extension.append(age_extension)
# karyotypic_sex
if 'karyotypicSex' in obj:
karyotypic_sex_extension = extension.Extension()
karyotypic_sex_extension.url = PHENOPACKETS_ON_FHIR_MAPPING[
'individual']['karyotypicSex']['url']
karyotypic_sex_extension.valueCodeableConcept = codeableconcept.CodeableConcept(
)
karyotypic_sex_extension.valueCodeableConcept.coding = list()
coding = c.Coding()
coding.display = obj.get('karyotypicSex', None)
coding.code = obj.get('karyotypicSex', None)
coding.system = PHENOPACKETS_ON_FHIR_MAPPING['individual'][
'karyotypicSex']['system']
karyotypic_sex_extension.valueCodeableConcept.coding.append(coding)
patient.extension.append(karyotypic_sex_extension)
# taxonomy
if 'taxonomy' in obj:
taxonomy_extension = extension.Extension()
taxonomy_extension.url = PHENOPACKETS_ON_FHIR_MAPPING['individual'][
'taxonomy']
taxonomy_extension.valueCodeableConcept = codeableconcept.CodeableConcept(
)
taxonomy_extension.valueCodeableConcept.coding = list()
coding = c.Coding()
coding.display = obj.get('taxonomy', None).get('label', None)
coding.code = obj.get('taxonomy', None).get('id', None)
taxonomy_extension.valueCodeableConcept.coding.append(coding)
patient.extension.append(taxonomy_extension)
return patient.as_json()
def _create_FHIRDate(date):
"""
Creates a FHIRDate object
:param date: datetime object used to set the date in the FHIRDate object
:returns: FHIRDate object
"""
eastern = timezone('US/Eastern')
FHIRDate = fd.FHIRDate()
FHIRDate.date = date.astimezone(eastern)
return FHIRDate
def fhir_gen_datetime(value=None, to_date=False, error_out=False):
"""
:return:
fhirclient.models.fhirdate.FHIRDate class object if it can be constructed
If invalid datetime string is passed, DatetimeParseError is raised if error_out=True
"""
dt = validate_datetime(value=value, to_date=to_date, error_out=error_out)
fhir_date_obj = fhirdate.FHIRDate()
if dt:
fhir_date_obj.date = dt
return fhir_date_obj
fhir_date_obj.date = None
return fhir_date_obj
def init_aqr(q):
aqr = QR.QuestionnaireResponse(f.aqr_templ(q.url),
strict=False) # resets aqr and contained q
aqr.questionnaire = Ref.FHIRReference(
{'reference':
'#contained-adaptive-{}'.format(q.id)}) # update the questionnaire
aqr.id = q.url.rsplit('/')[-1].replace(
'questionnaire', 'adaptive-questionnaireresponse') # update the QR id
dt = '{}Z'.format(datetime.utcnow().isoformat()) # start time...
aqr.authored = FD.FHIRDate(dt) # update datetime
# application.logger.info(json.dumps(qr.as_json(), indent=4, sort_keys=True))
# ---create narrative only answered and static questions ---
logging.info(aqr)
# get_next_q(q, aqr)
return (aqr)
def phenopacket_to_fhir(obj):
"""Converts Phenopacket to FHIR Composition.
:param obj: Phenopacket json
:return: FHIR Composition json
"""
schema_path = os.path.join(SCHEMA_PATH, 'phenopacket_schema.json')
try:
validate_schema(schema_path, obj)
except jsonschema.exceptions.ValidationError:
raise Exception("The phenopacket object is not valid.")
composition = comp.Composition()
if 'id' in obj:
composition.id = obj['id']
composition.subject = fhirreference.FHIRReference()
if 'subject' in obj:
composition.subject.reference = str(obj['subject']['id'])
composition.title = PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['title']
# elements in Composition required by FHIR spec
composition.status = 'preliminary'
composition.author = []
author = fhirreference.FHIRReference()
author.reference = obj['metaData']['createdBy']
composition.author.append(author)
composition.date = fhirdate.FHIRDate(obj['metaData']['created'])
composition.type = fhir_codeable_concept({
"id":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['code']['code'],
"label":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['title'],
"system":
PHENOPACKETS_ON_FHIR_MAPPING['phenopacket']['code']['system']
})
composition.section = []
# TODO add htsFiles to the list and fix inconsistency in mappings
sections = ['biosamples', 'variants', 'diseases']
for section in sections:
if obj[section]:
section_content = _get_section_object(obj.get(section, None),
section)
composition.section.append(section_content)
return composition.as_json()
def to_fhir_obj(self):
patient = p.Patient()
identifier = p.identifier.Identifier()
identifier.value = self.id
patient.identifier = [identifier]
name = hn.HumanName()
name.given = [self.first_name]
name.family = self.last_name
patient.name = [name]
patient.gender = self.sex
patient.birthDate = fd.FHIRDate(str(datetime.utcnow().date()))
# prints patient's JSON representation, now with id and name
address = a.Address()
address.state = self.state
address.district = self.district
patient.address = [address]
json = patient.as_json()
return patient
def initalize_report(self):
patient_reference = reference.FHIRReference(
{"reference": "Patient/" + self.patientID})
self.report.id = "dr-" + uuid4().hex[:13]
self.report.meta = meta.Meta({
"profile": [("http://hl7.org/fhir/uv/genomics-reporting" +
"/StructureDefinition/genomics-report")]
})
self.report.status = "final"
self.report.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "81247-9",
"display": "Master HL7 genetic variant reporting panel"
}]
})
self.report.subject = patient_reference
self.report.issued = date.FHIRDate(get_fhir_date())
self.report.contained = []
def fhir_patient(obj):
""" Converts Individual to FHIR Patient. """
patient = p.Patient()
patient.id = obj['id']
patient.birthDate = fhirdate.FHIRDate(obj.get('date_of_birth', None))
patient.gender = obj.get('sex', None)
patient.active = obj.get('active', None)
patient.deceasedBoolean = obj.get('deceased', None)
patient.extension = list()
# age
if 'age' in obj.keys():
age_extension = fhir_age(
obj, PHENOPACKETS_ON_FHIR_MAPPING['individual']['age'], 'age')
patient.extension.append(age_extension)
# karyotypic_sex
karyotypic_sex_extension = extension.Extension()
karyotypic_sex_extension.url = PHENOPACKETS_ON_FHIR_MAPPING['individual'][
'karyotypic_sex']['url']
karyotypic_sex_extension.valueCodeableConcept = codeableconcept.CodeableConcept(
)
karyotypic_sex_extension.valueCodeableConcept.coding = list()
coding = c.Coding()
coding.display = obj.get('karyotypic_sex', None)
coding.code = obj.get('karyotypic_sex', None)
coding.system = PHENOPACKETS_ON_FHIR_MAPPING['individual'][
'karyotypic_sex']['system']
karyotypic_sex_extension.valueCodeableConcept.coding.append(coding)
patient.extension.append(karyotypic_sex_extension)
# taxonomy
if 'taxonomy' in obj.keys():
taxonomy_extension = extension.Extension()
taxonomy_extension.url = PHENOPACKETS_ON_FHIR_MAPPING['individual'][
'taxonomy']
taxonomy_extension.valueCodeableConcept = codeableconcept.CodeableConcept(
)
taxonomy_extension.valueCodeableConcept.coding = list()
coding = c.Coding()
coding.display = obj.get('taxonomy', None).get('label', None)
coding.code = obj.get('taxonomy', None).get('id', None)
taxonomy_extension.valueCodeableConcept.coding.append(coding)
patient.extension.append(taxonomy_extension)
return patient.as_json()
def qr_view():
working_q = '{}_wq'.format(session['form_url'])
qr_working_q = cache.get(working_q)
reset_q_cache()
# results = request.form # this is multiDict structure - can only retreive the first element or use the get list method
application.logger.info('results = {}'.format(request.form))
# bp() # ***************** this is a break******************
# split out linkId and text to create answers create a QR resource use a template for the meta for now
qr = QR.QuestionnaireResponse(f.qr_templ, strict=False)
qr.questionnaire = Ref.FHIRReference({'reference': qr_working_q.url}) # update the questionnaire
qr.id = qr_working_q.url.rsplit('/')[-1].replace('questionnaire', 'questionnaireresponse') # update the QR id
dt = '{}Z'.format(datetime.utcnow().isoformat()) # == stop time
qr.extension = [get_responseperiod_x(session['q_starttime'],dt)] # add extension for FHIRDates start and stoptime
qr.authored = FD.FHIRDate(dt) # update datetime
# application.logger.info(json.dumps(qr.as_json(), indent=4, sort_keys=True))
qr.item = []
get_qr_items(request.form, qr_working_q.item, qr.item)
#application.logger.info(json.dumps(qr.as_json(), indent=4, sort_keys=True))
# ---create narrative---
qr.text.div = '''<div xmlns=\"http://www.w3.org/1999/xhtml\">
<h3>Response Summary</h3>
<strong>Questionnaire URL:</strong> {q}<br />
<strong>Date Completed:</strong> {d}<br />
<hr /> {r} <br /><br />
</div>'''.format(q=qr.questionnaire.reference, d=qr.authored.as_json(), r=''.join(session['narr_list']))
#scrub id for user POST and save as session qr
qr.identifier.value = qr.id
session['qr_id'] = qr.id # this is for saving my examples
session['profile'] = qr.meta.profile[0] # this is for uploads if validate by profile
# qr.id = None # TODO comment out for user posting to server
set_cache(qr.identifier.value, json.dumps(qr.as_json(),indent = 3, sort_keys=True))
# bp() # ***************** this is a break******************
return render_template('qr_view.html', qr=qr, qr_string=cache.get(qr.identifier.value), adaptive=False )
def write(server_url, system, code, patient, value):
observation = o.Observation()
observation.status = "final"
observation.subject = fr.FHIRReference()
observation.subject.reference = ("Patient/%s" % patient)
observation.code = cc.CodeableConcept()
tmp = c.Coding()
tmp.system = system
tmp.code = code
observation.code.coding = [tmp]
observation.valueString = str(value)
observation.effectiveDateTime = fd.FHIRDate("2017-11-28T00:00:00+00:00")
#write json obj
json_obj = observation.as_json()
json_obj["resourceType"] = "Observation"
print(json.dumps(json_obj))
requests.post(server_url + "Observation", json=json_obj)
def get_next_q(q, aqr, score=0): # assuming no nesting
cq = aqr.contained[0]
if cq.item is None: # create first response
cq.item = [] # use first item
get_hidden_score(cq.item) # add score as item 0
# add score as item 0
get_hidden_stddev(cq.item) # add stddev as item 1
q_item = get_q_items(q.item)[0]
q_item.definition = '{}-{}'.format(q.url, q_item.linkId)
# logging.info(q_item.prefix)
cq.item.append(q_item) # use first question
get_itemorder(cq.item[2], 3)
cq.copyright = q.copyright
dt = '{}Z'.format(datetime.utcnow().isoformat())
cq.date = FD.FHIRDate(dt)
cq.id = 'contained-adaptive-{}'.format(q.id)
cq.extension = q.extension
#cq.title = 'Contained Adaptive {}'.format(q.title)
#cq.url = q.url
cq = aqr.contained[0]
aqr.text.div = intro.format(t=cq.title,
c=cq.copyright,
q=cq.url,
d=cq.date.as_json()) # render intro
elif len(cq.item) < 5: # check number of q is < 5 add a new q
logging.info('cq.item = {}'.format(cq.item))
check_list = [x.linkId for x in cq.item]
logging.info('check_list = {}'.format(check_list))
next_cq_item = get_next_q_item(q.url, q.item, check_list)
logging.info('next_cq_item = {}'.format(next_cq_item))
get_itemorder(next_cq_item, len(cq.item) + 1)
cq.item.append(next_cq_item)
else:
# done change the status of the QR to complete and add score as a hidden # QUESTION:
aqr.status = 'completed'
return (aqr)
def addObservationToServer(self, patient_id, value_code, value_unit,
value_quantity, coding_code, coding_display,
coding_system, timestamp):
observation = o.Observation()
# Create Value Quantity
quantity = q.Quantity()
quantity.code = value_code
quantity.unit = value_unit
quantity.value = value_quantity
observation.valueQuantity = quantity
# Create Coding
code = cc.CodeableConcept()
coding_item = c.Coding()
coding_item.code = coding_code
coding_item.display = coding_display
coding_item.system = coding_system
coding_list = [coding_item]
code.coding = coding_list
observation.code = code
# Create Subject
reference = r.FHIRReference()
reference.reference = self.getPatientById(patient_id).relativePath()
observation.subject = reference
# Create Status
observation.status = 'final'
# Create Issued/EffectiveDateTime
observation.effectiveDateTime = d.FHIRDate(timestamp)
# Write the observation
result_json = observation.create(self.smart.server)
observation.id = self.getCreatedId(result_json)
return observation.id
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')
# 3 records, modify if more are required
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,
def getEmptyFhirJSON(patientID, start, end, refSeq, nocall=False):
logging.info("[FHIR: Converting XML to JSON]")
noCallRegion, queryRange = getNoCallableData(NO_CALL_FILE,
QUERY_RANGE_FILE)
nocallRows = len(noCallRegion.index)
start = queryRange.at[0, "START"]
end = queryRange.at[0, "END"]
if not noCallRegion.empty:
if noCallRegion['START'].iloc[0] <= queryRange['START'].iloc[0]:
noCallRegion['START'].iloc[0] = queryRange['START'].iloc[0]
if noCallRegion['END'].iloc[-1] >= queryRange['END'].iloc[0]:
noCallRegion['END'].iloc[-1] = queryRange['END'].iloc[0]
else:
nocall = False
contained_uid = uuid4().hex[:13]
patient_reference = reference.FHIRReference(
{"reference": "Patient/" + patientID})
variant_reference = reference.FHIRReference(
{"reference": "#rs-" + contained_uid})
report = dr.DiagnosticReport()
report.id = "dr-" + uuid4().hex[:13]
report.meta = meta.Meta({
"profile": [
"http://hl7.org/fhir/uv/genomics-reporting/StructureDefinition/diagnosticreport"
]
})
report.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "53041-0",
"display": "DNA region of interest panel"
}]
})
report.status = "final"
report.subject = patient_reference
report.issued = date.FHIRDate(Utilities.getFhirDate())
report.result = [variant_reference]
observation_rs = observation.Observation()
contained_rs = observation_rs
observation_dv = observation.Observation()
contained_dv = observation_dv
observation_sid = observation.Observation()
contained_sid = observation_sid
contained_rs.id = "rs-" + contained_uid
observation_rs.resource_type = "Observation"
contained_rs.meta = meta.Meta({
"profile": [
"http://hl7.org/fhir/uv/genomics-reporting/StructureDefinition/obs-region-studied"
]
})
observation_rs.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "53041-0",
"display": "DNA region of interest panel"
}]
})
observation_rs.status = "final"
observation_rs.category = [
concept.CodeableConcept({
"coding": [{
"system":
"http://terminology.hl7.org/CodeSystem/observation-category",
"code": "laboratory"
}]
})
]
observation_rs.subject = patient_reference
observation_rs_component1 = observation.ObservationComponent()
observation_rs_component1.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "51959-5",
"display": "Range(s) of DNA sequence examined"
}]
})
observation_rs_component1.valueRange = valRange.Range({
"low": {
"value": int(start)
},
"high": {
"value": int(end)
}
})
observation_rs_component2 = observation.ObservationComponent()
observation_rs_component2.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "tbd-coordinate system",
"display": "Genomic coordinate system"
}]
})
observation_rs_component2.valueCodeableConcept = concept.CodeableConcept({
"coding": [{
"system":
"http://hl7.org/fhir/uv/genomics-reporting/CodeSystem/genetic-coordinate-system",
"code": "1"
}]
})
observation_rs_component3 = observation.ObservationComponent()
observation_rs_component3.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "48013-7",
"display": "Genomic reference sequence ID"
}]
})
observation_rs_component3.valueCodeableConcept = concept.CodeableConcept({
"coding": [{
"system": "http://www.ncbi.nlm.nih.gov/nuccore",
"code": refSeq
}]
})
if nocall:
#component Region-studied (No call region)
nocallComponents = []
for i in range(nocallRows):
nocallComponents.append('observation_rs_component' + str(i + 4))
observation_rs_components = []
for (index, i) in zip(noCallRegion.index, nocallComponents):
i = observation.ObservationComponent()
i.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "TBD-noCallRegion",
"display": "No call region"
}]
})
observation_rs_components.append(i)
if nocall:
observation_rs.component = [
observation_rs_component2, observation_rs_component3
] + observation_rs_components
else:
observation_rs.component = [
observation_rs_component2, observation_rs_component3
]
response = report.as_json()
od = OrderedDict()
od["resourceType"] = response['resourceType']
od["id"] = response['id']
od["meta"] = response['meta']
od["contained"] = response['contained']
od["status"] = response['status']
od["code"] = response['code']
od["subject"] = response['subject']
od["issued"] = response['issued']
od["result"] = response['result']
with open(FHIR_JSON_RESULT + 'fhir.json', 'w') as fp:
json.dump(od, fp, indent=4)
return FHIR_JSON_RESULT
def effective_date_time(self):
return fd.FHIRDate(str(datetime.now()))
def create_fhir_object(self):
"""
method: create_fhir_object()
Create a fhir-client.model.address.Adress object and stores to the self.fhir attribute
:return:
None
"""
# Initialize FHIRclient Address object
# Assign Address object attributes to FHIRclient Address object
fa = fhir_address.Address()
if self.address1:
fa.line = [self.address1]
if self.address2:
fa.line.append(self.address2)
if self.city:
fa.city = self.city
if self.state:
fa.state = self.state
if self.zipcode:
fa.postalCode = self.zipcode
fa.text = self.formatted_address()
if isinstance(self.start_date, date) or isinstance(self.end_date, date):
p = period.Period()
if self.start_date:
fhirdate_start = fhirdate.FHIRDate()
fhirdate_start.date = self.start_date
p.start = fhirdate_start
if self.end_date:
fhirdate_end = fhirdate.FHIRDate()
fhirdate_end.date = self.end_date
p.end = fhirdate_end
fa.period = p
if self.use:
fa.use = self.use.lower()
if self.is_postal and self.is_physical:
fa.type = 'both'
elif self.is_postal:
fa.type = 'postal'
elif self.is_physical:
fa.type = 'physical'
if self.district:
fa.district = self.district
if self.country:
fa.country = self.country
self.fhir = fa
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')
