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 add_phased_relationship_obv(self):
patient_reference = reference.FHIRReference(
{"reference": "Patient/" + self.patientID})
self.sequence_rels \
= get_sequence_relation(self.phased_rec_map)
for index in self.sequence_rels.index:
siduid = "sid-" + uuid4().hex[:13]
self.result_ids.append(siduid)
observation_sid = observation.Observation()
observation_sid.resource_type = "Observation"
observation_sid.id = siduid
observation_sid.meta = meta.Meta({
"profile":
[("http://hl7.org/fhir/uv/" + "genomics-reporting/" +
"StructureDefinition/" + "sequence-phase-relationship")]
})
observation_sid.status = "final"
observation_sid.category = [
concept.CodeableConcept({
"coding": [{
"system": ("http://terminology.hl7.org/" +
"CodeSystem/observation-category"),
"code":
"laboratory"
}]
})
]
observation_sid.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "82120-7",
"display": "Allelic phase"
}]
})
observation_sid.subject = patient_reference
observation_sid.valueCodeableConcept = concept.CodeableConcept({
"coding": [{
"system":
("http://hl7.org/fhir/uv/" + "genomics-reporting/" +
"CodeSystem/seq-phase-relationship"),
"code":
self.sequence_rels.at[index, 'Relation'],
"display":
self.sequence_rels.at[index, 'Relation']
}]
})
self.report.contained.append(observation_sid)
def get_codeable_concept(system, code, display=None):
codeable_concept = {"coding": [{}]}
codeable_concept['coding'][0]['system'] = system
codeable_concept['coding'][0]['code'] = code
if display is not None:
codeable_concept['coding'][0]['display'] = display
return concept.CodeableConcept(codeable_concept)
def to_fhir(self):
route_code = codeableconcept.CodeableConcept()
route_code.coding = [
route_coding.to_fhir() for route_coding in self.coding
]
return route_code
def _get_section_object(nested_obj, title):
"""Internal function to convert phenopacket m2m objects to Composition section.
:param nested_obj: m2m relationship object
:param title: field name that holds m2m relationship
:return: section content object
"""
section_content = comp.CompositionSection()
section_values = PHENOPACKETS_ON_FHIR_MAPPING['phenopacket'][title]
section_content.title = section_values['title']
section_content.code = codeableconcept.CodeableConcept()
section_content.code.coding = []
coding = c.Coding()
coding.system = section_values['code']['system']
coding.version = section_values['code']['version']
coding.code = section_values['code']['code']
coding.display = section_values['code']['display']
section_content.code.coding.append(coding)
section_content.entry = []
for item in nested_obj:
entry = fhirreference.FHIRReference()
if item.get('id'):
entry.reference = str(item['id'])
elif item.get('uri'):
entry.reference = item['uri']
else:
# generate uuid when no 'id' or 'uri' present
entry.reference = str(uuid.uuid1())
section_content.entry.append(entry)
return section_content
def __init__(self, Organization=None):
"""
Uses fhirclient.models to create and post practitoner resource. Currently, using class variables.
:param smart: fhirclient.client.FHIRClient object.
:returns: practitioner id created by server
"""
if Organization is None:
self.Organization = generateorganization.GenerateOrganization(
).Organization
else:
self.Organization = Organization
Practitioner = pr.Practitioner()
PractitionerQualification = pr.PractitionerQualification()
CodeableConcept = cc.CodeableConcept()
Coding = c.Coding()
Coding.code = random.choice(['MD', 'DO'])
Coding.system = 'https://www.hl7.org/fhir/v2/0360/2.7/index.html'
CodeableConcept.coding = [Coding]
PractitionerQualification.code = CodeableConcept
Practitioner.qualification = [PractitionerQualification]
name = hn.HumanName()
self.family, self.given, Practitioner.gender = self._generate_person()
name.family = [self.family]
name.given = [self.given]
Practitioner.name = name
self._validate(Practitioner)
self.response = self.post_resource(Practitioner)
Practitioner.id = self._extract_id()
self.Practitioner = Practitioner
print(self)
def annotate(self, obsid):
obs = o.Observation()
obs.uuid = uuid.uuid4().urn
observation = o.Observation.read(obsid, self.smart.server)
obs.specimen = observation.specimen
obs.related = observation.related
obs.basedOn = observation.basedOn
obs.code = observation.code
obs.component = observation.component
obs.method = observation.method
seq_id = observation.related[0].target.reference.split("/")[1]
seq = s.Sequence.read(seq_id, self.smart.server)
obseverved_seq = seq.observedSeq
annotation = self.gfeapi.annotate_get(obseverved_seq, imgthla_version="3.31.0")
gfe = annotation.gfe
bodysite3 = cc.CodeableConcept()
bodySiteCoding3 = mkCoding(system='http://act.b12x.org',
code='261063000',
version="0.0.5")
bodysite3.coding = [bodySiteCoding3]
bodysite3.text = gfe
obs.valueCodeableConcept = bodysite3
obs.status = 'final'
obs.subject = observation.subject
return obs
def category(self):
return [cc.CodeableConcept({
"coding": [{
"system": "http://hl7.org/fhir/observation-category",
"code": "vital-signs",
"display": "Vital Signs"
}]
})]
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 category(self):
return cc.CodeableConcept({
"coding": [{
"system": "http://hl7.org/fhir/observation-category",
"code": "activity",
"display": "Activity"
}],
"text":
"Activity"
})
def code(self):
return cc.CodeableConcept({
"coding": [{
"system": "https://r.details.loinc.org/LOINC",
"code": "67775-7",
"display": "Level of responsiveness"
}],
"text":
"Level of responsiveness"
})
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 interpretation(self):
return cc.CodeableConcept({
"coding": [
{
"system": "http://hl7.org/fhir/v2/0078",
"code": "H",
"display": "High"
}
]
})
def interpretation(self):
return cc.CodeableConcept({
"coding": [
{
"system": "http://hl7.org/fhir/v2/0078",
"code": "N",
"display": "Normal"
}
],
"text": "Normal (applies to non-numeric results)"
})
def _get_region_studied_component(self, reportable_query_regions,
nocall_regions):
observation_rs_components = []
for _, row in reportable_query_regions.df.iterrows():
obv_comp = observation.ObservationComponent()
obv_comp.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "51959-5",
"display": "Ranges-examined component"
}]
})
obv_comp.valueRange = valRange.Range({
"low": {
"value": np.float(row['Start']) + 1
},
"high": {
"value": np.float(row['End']) + 1
}
})
observation_rs_components.append(obv_comp)
for _, row in nocall_regions.df.iterrows():
obv_comp = observation.ObservationComponent()
obv_comp.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "TBD-UncallableRegions",
"display": "Uncallable region"
}]
})
obv_comp.valueRange = valRange.Range({
"low": {
"value": np.float(row['Start']) + 1
},
"high": {
"value": np.float(row['End']) + 1
}
})
observation_rs_components.append(obv_comp)
return observation_rs_components
def category(self):
return [
cc.CodeableConcept({
"coding": [{
"system":
"http://terminology.hl7.org/CodeSystem/observation-category",
"code": "vital-signs",
"display": "Vital Signs"
}],
"text":
"Vital Signs"
})
]
def fhir_codeable_concept(obj):
""" Generic function to convert object to FHIR CodeableConcept. """
codeable_concept = codeableconcept.CodeableConcept()
codeable_concept.coding = []
if isinstance(obj, list):
for item in obj:
coding = fhir_coding_util(item)
codeable_concept.coding.append(coding)
else:
coding = fhir_coding_util(obj)
codeable_concept.coding.append(coding)
return codeable_concept
def _create_FHIRCodeableConcept(self, code, system=None, display=None):
"""
Creates and returns a FHIRCodeableConcept object. References self._create_FHIRCoding()
:param self:
:param code: code from standard System
:param system: coding System
:param display: how the resource should be displayed
:returns: CodeableConcept FHIR object
"""
CodeableConcept = cc.CodeableConcept()
Coding = self._create_FHIRCoding(code, system, display)
CodeableConcept.coding = [Coding]
return CodeableConcept
def map_cc(concept):
# create CodeableConcept based on slot data
cc = CC.CodeableConcept()
cc.coding = []
cc.text = concept.text
try:
cc_coding = Coding.Coding()
for translation in concept.coding:
cc_coding.code = translation.code
cc_coding.display = translation.display
cc_coding.system = translation.system
cc.coding.append(cc_coding)
except TypeError: # coding missing
pass
return (cc)
def mksequence():
"""
make a Sequence resource using the data model
"""
import fhirclient.models.sequence as s
sequence = s.Sequence({
# 'id': 'seq-1',
'coordinateSystem': 0
})
# adding uuid attribute to the sequence object
# for later use in building bundles and referencing
# resources with fullUrl
sequence.uuid = uuid.uuid4().urn
import fhirclient.models.coding as c
coding = c.Coding()
coding.system = 'http://www.ebi.ac.uk/ipd/imgt/hla/'
coding.version = '3.23'
coding.display = 'HLA-A*01:01:01:01'
coding.code = 'HLA00001'
import fhirclient.models.codeableconcept as cc
codecon = cc.CodeableConcept()
codecon.coding = [coding]
codecon.text = 'HLA-A*01:01:01:01'
refseq = s.SequenceReferenceSeq()
refseq.referenceSeqId = codecon
refseq.windowStart = 503
refseq.windowEnd = 773
sequence.type = 'dna'
sequence.referenceSeq = refseq
sequence.observedSeq = (
'GCTCCCACTCCATGAGGTATTTCTTCACATCCGTGTCCCGGCCCGGCCGCGGGGAGCCCC'
'GCTTCATCGCCGTGGGCTACGTGGACGACACGCAGTTCGTGCGGTTCGACAGCGACGCCG'
'CGAGCCAGAAGATGGAGCCGCGGGCGCCGTGGATAGAGCAGGAGGGGCCGGAGTATTGGG'
'ACCAGGAGACACGGAATATGAAGGCCCACTCACAGACTGACCGAGCGAACCTGGGGACCC'
'TGCGCGGCTACTACAACCAGAGCGAGGACG')
import fhirclient.models.narrative as n
narrative = n.Narrative()
narrative.div = ('<div xmlns="http://www.w3.org/1999/xhtml">'
'<pre>HLA-A*01:01:01:01, exon 3</pre>'
'</div>')
narrative.status = 'generated'
sequence.text = narrative
return sequence
def _add_codeable_value(self, Observation, measurement):
"""
Adds a codeableconcept value object to Observation.
:param Observation: fhirclient.models.observation.Observation object
:param measurement: measurement dictionary
:returns: Observation FHIR object
"""
CodeableConcept = cc.CodeableConcept()
Coding = c.Coding()
Coding.system = 'http://loinc.org'
Coding.code = self.observation_dict[measurement]['value_loinc']
Coding.display = self.observation_dict[measurement]['value_display']
CodeableConcept.coding = [Coding]
Observation.valueCodeableConcept = CodeableConcept
return Observation
def build_keyword(study_keywords):
"""
Build the keywords up
:param keywords:
:return:
"""
keywords = []
for term in study_keywords:
cc = codeableconcept.CodeableConcept()
cc.text = term
cc.coding = [
coding.Coding(
dict(code=quote(term),
system="https://example.org",
display=term))
]
keywords.append(cc)
return keywords
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 build_focus(mesh_terms):
"""
Build the ResearchStudy.focus
:param conditions:
:param interventions:
:return:
"""
# assuming mesh term here
focus = []
for study_term_type, terms in mesh_terms.items():
print("{}: {}".format(study_term_type, terms))
for term in terms:
cc = codeableconcept.CodeableConcept()
cc.text = term
cc.coding = [
coding.Coding(
dict(code=term, system="https://mesh.org", display=""))
]
focus.append(cc)
return focus
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 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 _get_section_object(nested_obj, title):
""" Internal function to convert phenopacket m2m objects to Composition section. """
section_content = comp.CompositionSection()
section_values = PHENOPACKETS_ON_FHIR_MAPPING['phenopacket'][title]
section_content.title = section_values['title']
section_content.code = codeableconcept.CodeableConcept()
section_content.code.coding = []
coding = c.Coding()
coding.system = section_values['code']['system']
coding.version = section_values['code']['version']
coding.code = section_values['code']['code']
coding.display = section_values['code']['display']
section_content.code.coding.append(coding)
section_content.entry = []
for item in nested_obj:
entry = fhirreference.FHIRReference()
if item.get('id'):
entry.reference = str(item['id'])
else:
entry.reference = item['uri']
section_content.entry.append(entry)
return section_content
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')
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 add_regionstudied_obv(self, ref_seq, reportable_query_regions,
nocall_regions):
if reportable_query_regions.empty and nocall_regions.empty:
return
patient_reference = reference.FHIRReference(
{"reference": "Patient/" + self.patientID})
contained_uid = "rs-" + uuid4().hex[:13]
self.result_ids.append(contained_uid)
# Region Studied Obeservation
observation_rs = observation.Observation()
contained_rs = observation_rs
contained_rs.id = contained_uid
observation_rs.resource_type = "Observation"
contained_rs.meta = meta.Meta({
"profile": [("http://hl7.org/fhir/uv/" + "genomics-reporting/" +
"StructureDefinition/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_component2 = observation.ObservationComponent()
observation_rs_component2.code = concept.CodeableConcept({
"coding": [{
"system": "http://loinc.org",
"code": "92822-6",
"display": "Genomic coord system"
}]
})
observation_rs_component2\
.valueCodeableConcept = concept.CodeableConcept(
{
"coding": [
{
"system": "http://loinc.org",
"code": "LA30102-0",
"display": "1-based character counting"
}
]
}
)
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": ref_seq
}
]
}
)
observation_rs_components = self._get_region_studied_component(
reportable_query_regions, nocall_regions)
observation_rs.component = [
observation_rs_component2, observation_rs_component3
] + observation_rs_components
# Observation structure : described-variants
self.report.contained.append(contained_rs)
