def getaverageage():
fhir = FHIR()
patients = fhir.get_all_patients()
ages = []
for patient in patients:
ages.append(patient.age())
return str(sum(ages) / len(ages))
def surname(text):
fhir = FHIR()
patients = fhir.get_all_patients()
number_of_patients = 0
for i in patients:
number_of_patients = number_of_patients + 1
for index in range(0, number_of_patients):
patient = patients[index]
if text == patient.name.family:
return index
return -1
def unique_identifier(text):
fhir = FHIR()
patients = fhir.get_all_patients()
number_of_patients = 0
for i in patients:
number_of_patients = number_of_patients + 1
for index in range(0, number_of_patients):
patient = patients[index]
if text == patient.uuid:
return index
return -1
def graph_by_states():
fhir = FHIR()
patients = fhir.get_all_patients()
states = {}
for patient in patients:
for addressArr in patient.addresses:
states.update({addressArr.state: states.get(addressArr.state, 0) + 1})
plt.bar(range(len(states)), list(states.values()), align='center')
plt.xticks(range(len(states)), list(states.keys()), rotation='vertical')
plt.show()
def __init__(self, page=None, patient_uuid=None):
fhir = FHIR()
self.uuid_presented = False
self.patients = fhir.get_all_patients()
if patient_uuid is not None:
self.uuid_presented = True
self.specific_patient = fhir.get_patient(patient_uuid)
if page is not None:
try:
assert patient_uuid is None
self.patients = fhir.get_patient_page(page)
except AssertionError:
sys.exit(
"Error: patients page and patient uuid cannot be initialised at the same time"
)
def graph_languages_spoken():
fhir = FHIR()
patients = fhir.get_all_patients()
languages = {}
for patient in patients:
for language in patient.communications.languages:
languages.update({language: languages.get(language, 0) + 1})
plt.bar(range(len(languages)), list(languages.values()), align='center')
plt.xticks(range(len(languages)), list(languages.keys()), rotation='vertical')
fig1 = plt.gcf()
plt.draw()
number = random.randint(1, 1000)
fig1.savefig('plot'+str(number)+'.png', dpi=100)
return number
def graph_by_cities():
fhir = FHIR()
patients = fhir.get_all_patients()
cities = {}
for patient in patients:
for addressArr in patient.addresses:
cities.update({addressArr.city: cities.get(addressArr.city, 0) + 1})
plt.bar(range(len(cities)), list(cities.values()), align='center')
plt.xticks(range(len(cities)), list(cities.keys()), rotation='vertical')
# fig = plt.figure()
# fig.savefig('plot.png')
plt.show()
# need to think of what can we graph from the observation data
# def graph_by_diseases():
# fhir = FHIR()
# patients = fhir.get_all_patients()
# problems = {}
# count = 0
# for patient in patients:
# count = count + 1
# if count == 4:
# break
# print("YOOOO THIS IS THR FKING ID" + patient.uuid)
# try:
# observations = fhir.get_patient_observations(patient.uuid)
# except:
# continue
# for observation in observations:
# print("observation 1")
# components = observation.components
# for component in components:
# print(component.display)
# print(component.value)
# problems.update({component.display: problems.get(component.display, 0) + 1})
# plt.bar(range(len(problems)), list(problems.values()), align = 'center')
# plt.xticks(range(len(problems)),list(problems.keys()), rotation = 'vertical' )
# plt.show();
# def graph_by_diseases():
# def graph_by_certain_age(age):
def graph_marital_status():
fhir = FHIR()
patients = fhir.get_all_patients()
marital_status = {}
for patient in patients:
if str(patient.marital_status) in marital_status:
marital_status[str(patient.marital_status)] += 1
else:
marital_status[str(patient.marital_status)] = 1
plt.bar(range(len(marital_status)), list(marital_status.values()), align='center')
plt.xticks(range(len(marital_status)), list(marital_status.keys()))
fig1 = plt.gcf()
plt.draw()
number = random.randint(1, 1000)
fig1.savefig('plot'+str(number)+'.png', dpi=100)
return number
class Main:
def __init__(self):
self.fhir = FHIR('https://localhost:5001/api/', verify_ssl=False)
self.patients = self.fhir.get_all_patients()
def print_patients_in_range(self, limit, lower, upper, sender_name, sender_ref, app_url):
i = 0
patients_list = []
for patient in self.patients:
dict = {}
if not i < limit:
# print(patients_list)
return patients_list
id = patient.uuid
name = patient.full_name()
age = patient.age()
if age >= lower and age < upper:
telecoms = patient.telecoms
phone = telecoms[len(telecoms) - 1].number
# print(phone)
msg = "Hi " + name + ",\n" + "Are you a carer? If so, we've built an app just for you! You can download our app for carers here: " + app_url + "\nUse this reference code to set up your app: " + sender_ref + "\nTake Care,\n" + sender_name
# print(msg)
# print(id)
# print(name)
# print(age)
# print()
dict["phone"] = phone
dict["msg"] = msg
patients_list.append(dict)
i += 1
# main = Main()
# list = main.print_patients_in_range(3, 55, 65, "Paul", "UCL-FHIR-Hack", "appdownload.com")
# patient = fhir.get_patient('8f789d0b-3145-4cf2-8504-13159edaa747')
# name = patient.full_name()
# print(patient)
# list = fhir.get_all_patients()
def graph_by_age_groups():
fhir = FHIR()
patients = fhir.get_all_patients()
ageGroups = {'<10': 0, '10-20': 0, '20-30':0, '30-40':0, '40-50':0, '50-60':0, '60-70': 0, '70-80':0, '80-90':0, '90+':0}
for patient in patients:
birth = patient.birth_date
patientAge = calculate_age(birth)
if(patientAge < 10):
ageGroups['<10'] += 1
elif( 10 <= patientAge < 20):
ageGroups['10-20'] += 1
elif( 20 <=patientAge < 30):
ageGroups['20-30'] += 1
elif( 30 <=patientAge < 40):
ageGroups['30-40'] += 1
elif( 40 <=patientAge< 50):
ageGroups['40-50'] += 1
elif( 50 <=patientAge < 60):
ageGroups['50-60'] += 1
elif( 60 <patientAge < 70 ):
ageGroups['60-70'] += 1
elif( 70 <patientAge < 80 ):
ageGroups['70-80'] += 1
elif( 80 <patientAge < 90 ):
ageGroups['80-90'] += 1
elif( 90 <=patientAge ):
ageGroups['90+'] += 1
plt.bar(range(len(ageGroups)), list(ageGroups.values()), align='center')
plt.xticks(range(len(ageGroups)), list(ageGroups.keys()), rotation='vertical')
fig1 = plt.gcf()
plt.draw()
number = random.randint(1, 1000)
fig1.savefig('plot'+str(number)+'.png', dpi=100)
return number
def observationstats():
fhir = FHIR()
patients = fhir.get_all_patients()
observations = []
for patient in patients:
observations.extend(fhir.get_patient_observations(patient.uuid))
total_obsv = len(observations)
observation_types = [observation.type for observation in observations]
most_frequent_observation_type = max(set(observation_types),
key=observation_types.count)
observation_components = []
for observation in observations:
observation_components.extend(observation.components)
total_obsv_components = len(observation_components)
observation_component_types = [
observation_component.display
for observation_component in observation_components
]
most_frequent_observation_component_type = max(set(observation_types),
key=observation_types.count)
obsvstats_dictionary = {
"totalObsv": total_obsv,
"mfObservationType": most_frequent_observation_type,
"totalObsvComp": total_obsv_components,
"mfObsvCompType": most_frequent_observation_component_type
}
response = jsonify(obsvstats_dictionary)
return response
def __init__(self):
fhir = FHIR(endpoint='https://fhir.compositegrid.com:5001/api/')
self.patients = fhir.get_all_patients()
class GraphManager:
"""Create a FHIR object to retrieve patient data """
def __init__(self):
self.FHIR = FHIR()
def get_patients(self, page_number: int = 0) -> List[Patient]:
if page_number == 0:
return self.FHIR.get_all_patients()
else:
return self.FHIR.get_patient_page(page_number)
def characteristic_is_observation_type(self, characteristic: str) -> bool:
non_observation_types = ['language', 'marital status', 'city', 'country', 'state', 'family name', 'age']
if non_observation_types.__contains__(characteristic):
return False
return True
def get_latest_obeservation_with_characteristic(self, patient: Patient, characteristic: str):
observations = self.FHIR.get_patient_observations_page(patient.uuid, 3)
latest_observation_value = 0
latest_time = datetime(1,1,1)
found_first_observation = False
for observation in observations:
for observation_component in observation.components:
if observation_component.display == characteristic:
if found_first_observation == False:
found_first_observation = True
latest_time = observation.effective_datetime
latest_observation_value = observation_component.value
elif observation.effective_datetime > latest_time:
latest_time = observation.effective_datetime
latest_observation_value = observation_component.value
return latest_observation_value
def get_data(self, characteristic: str, page_number: int = 0):
characteristic_values = {}
patients = self.get_patients(page_number)
if self.characteristic_is_observation_type(characteristic) == True:
for patient in patients:
data_point = self.get_latest_obeservation_with_characteristic(patient, characteristic)
if str(data_point) in characteristic_values:
characteristic_values[str(data_point)] += 1
else:
characteristic_values[str(data_point)] = 1
else:
for patient in patients:
if characteristic == 'language':
for language in patient.communications.languages:
characteristic_values.update({language: characteristic_values.get(language, 0) + 1})
if characteristic == 'marital status':
if str(patient.marital_status) in characteristic_values:
characteristic_values[str(patient.marital_status)] += 1
else:
characteristic_values[str(patient.marital_status)] = 1
if characteristic == 'city':
if patient.addresses[0].city in characteristic_values:
characteristic_values[str(patient.addresses[0].city)] += 1
else:
characteristic_values[str(patient.addresses[0].city)] = 1
if characteristic == 'country':
if patient.addresses[0].country in characteristic_values:
characteristic_values[str(patient.addresses[0].country)] += 1
else:
characteristic_values[str(patient.addresses[0].country)] = 1
if characteristic == 'state':
if patient.addresses[0].state in characteristic_values:
characteristic_values[str(patient.addresses[0].state)] += 1
else:
characteristic_values[str(patient.addresses[0].state)] = 1
if characteristic == 'family name':
if patient.name.family in characteristic_values:
characteristic_values[str(patient.name.family)] += 1
else:
characteristic_values[str(patient.name.family)] = 1
if characteristic == 'age':
if floor(patient.age()) in characteristic_values:
characteristic_values[str(floor(patient.age()))] += 1
else:
characteristic_values[str(floor(patient.age()))] = 1
return collections.OrderedDict(sorted(characteristic_values.items()))
def graph_single_characteristic(self, characteristic: str, page_number: int = 0, graph_type: str = 'x_y_plot'):
data = self.get_data(characteristic, page_number)
plt.title('Values of the ' + str(characteristic) + ' in the first ' + str(page_number) + ' pages in the patients record')
if graph_type == 'bar chart':
plt.bar(range(len(data)), list(data.values()), align='center')
plt.xticks(range(len(data)), list(data.keys()), rotation='vertical')
elif graph_type == 'pie chart':
plt.pie(data.values(), labels=data.keys(), autopct='%1.1f%%',
shadow=True, startangle=90)
plt.axis('equal')
elif graph_type == 'x_y_plot':
plt.plot(list(data.keys()), list(data.values()))
def graph_multiple_characteristics(self, characteristics: List[str], page_number: int = 0, graph_type: str = 'x_y_plot'):
for characteristic in characteristics:
plt.figure()
self.graph_single_characteristic(characteristic, page_number, graph_type)
def show_figures(self):
plt.show()
class SyntheticDataGenerator():
def __init__(self):
self.fhir = FHIR()
self.patients = self.fhir.get_all_patients()
self.num_examples = len(self.patients)
self.num_features = 11
self.patient_record_columns = [
"UUID", "Name", "Gender", "Birthdate", "MaritalStatus",
"MultipleBirth", "CoreRace", "CoreEthnicity", "Birthsex",
"disabilityAdjLifeYears", "QualAdjLifeYears"
]
def getPatientsDF(self):
patients = self.patients
patient_data = np.zeros((self.num_examples, self.num_features),
dtype=object)
for i in range(self.num_examples):
patient = patients[i]
patient_data[i][0] = patient.uuid
patient_data[i][1] = patient.full_name()
patient_data[i][2] = patient.gender
patient_data[i][3] = patient.birth_date
patient_data[i][4] = patient.marital_status.marital_status
patient_data[i][5] = patient.multiple_birth
patient_data[i][6] = patient.get_extension("us-core-race")
patient_data[i][7] = patient.get_extension("us-core-ethnicity")
patient_data[i][8] = patient.get_extension("us-core-birthsex")
patient_data[i][9] = patient.get_extension(
"disability-adjusted-life-years")
patient_data[i][10] = patient.get_extension(
"quality-adjusted-life-years")
return pd.DataFrame(patient_data, columns=self.patient_record_columns)
def createObservations(self):
# patient_observations = []
# for uuid in patients_df["UUID"]:
# patient_observations.append(fhir.get_patient_observations(uuid))
with open("patient_obs.pkl", "rb") as f:
patient_observations = pickle.load(f)
components = []
for obs in patient_observations:
for ob in obs:
for component in ob.components:
components.append(component.display)
components_arr = np.unique(components)
for i in range(len(components_arr)):
components_arr[i] = components_arr[i].replace("\u200b", "")
components_arr = np.unique(components_arr)
components_dict = {}
for i in range(components_arr.size):
components_dict[components_arr[i]] = i
patient_observations_arr = np.empty(
(self.num_examples, components_arr.size))
patient_observations_arr[:] = np.nan
number_obs_arr = np.zeros(patient_observations_arr.shape, dtype=int)
for obs in patient_observations:
for ob in obs:
for component in ob.components:
components.append(component.display)
for i in range(len(patient_observations)):
for j in range(len(patient_observations[i])):
for k in range(len(patient_observations[i][j].components)):
ob = patient_observations[i][j].components[k]
index = (i,
components_dict[ob.display.replace("\u200b", "")])
if np.isnan(patient_observations_arr[index]):
patient_observations_arr[index] = ob.value
else:
patient_observations_arr[index] += ob.value
number_obs_arr[index] += 1
patient_mean_obs = patient_observations_arr / number_obs_arr
patient_observations_df = pd.DataFrame(patient_mean_obs,
columns=components_arr)
return patient_observations_df
def generateDataset(self, patient_observations_df):
patients_df = self.getPatientsDF()
collengths = []
for column in patient_observations_df.columns:
if len(patient_observations_df[column].dropna()) < 100:
collengths.append(column)
patient_observations_df.drop(collengths, axis=1, inplace=True)
full_patient_df = pd.concat([patients_df, patient_observations_df],
axis=1)
full_patient_df["Age"] = (date.today() -
full_patient_df["Birthdate"]).dt.days
full_patient_df["Gender"] = (full_patient_df["Gender"] == "male")
full_patient_df["MaritalStatus"] = (
full_patient_df["MaritalStatus"] == "M")
full_patient_df["Birthsex"] = (full_patient_df["Birthsex"] == "M")
full_patient_df.rename(
{
"Gender": "is_male",
"MaritalStatus": "is_married",
"Birthsex": "birthsex_is_male"
},
axis=1,
inplace=True)
full_patient_df["is_male"] = full_patient_df["is_male"].astype(int)
full_patient_df["is_married"] = full_patient_df["is_married"].astype(
int)
full_patient_df["MultipleBirth"] = full_patient_df[
"MultipleBirth"].astype(int)
full_patient_df["birthsex_is_male"] = full_patient_df[
"birthsex_is_male"].astype(int)
patient_dataset = full_patient_df.copy()
cols_to_drop = [
"Name", "is_male", "Birthdate", "is_married", "CoreRace", "UUID",
"CoreEthnicity", "MultipleBirth",
"Body mass index (BMI) [Percentile] Per age and gender",
"Weight-for-length Per age and sex", "RBC Auto (Bld) [#/Vol]"
]
patient_dataset["disabilityAdjLifeYears"] = patient_dataset[
"disabilityAdjLifeYears"].astype(float)
patient_dataset["QualAdjLifeYears"] = patient_dataset[
"QualAdjLifeYears"].astype(float)
patient_dataset.drop(cols_to_drop, axis=1, inplace=True)
return patient_dataset
def generateSyntheticData(self, number_of_datapoints):
patient_dataset = self.generateDataset(self.createObservations())
covariance_data = patient_dataset.cov() + 45 * np.identity(48)
covariance_data.fillna(0, inplace=True)
patient_cholesky = np.linalg.cholesky(covariance_data)
samples = np.random.multivariate_normal(patient_dataset.mean().values,
covariance_data,
size=number_of_datapoints * 2)
synthetic_df = pd.DataFrame(samples)
synthetic_df[0] = (synthetic_df[0] >= 1)
synthetic_df = synthetic_df.astype(int)
synthetic_df = synthetic_df[(synthetic_df[1] >= 0)
& (synthetic_df[2] >= 0)]
synthetic_df[synthetic_df < 0] = np.nan
synthetic_df.columns = patient_dataset.columns
synthetic_df["Age"] = synthetic_df["Age"] // 365
return synthetic_df.head(number_of_datapoints)
def setVariables(searchField, searchCriteria):
searchF = searchField
searchC = searchCriteria
fhir = FHIR()
global patients
patients = fhir.get_all_patients()
def index():
fhir = FHIR()
patients = fhir.get_all_patients()
return render_template('patients.html', patients=patients)
def demographicGroups():
fhir = FHIR()
patients = fhir.get_all_patients()
statistics = {
'age': {
'0 to 9': 0,
'10 to 19': 0,
'20 to 29': 0,
'30 to 39': 0,
'40 to 49': 0,
'50 to 59': 0,
'60 to 69': 0,
'70 to 79': 0,
'80 to 100': 0,
'> 100': 0
},
'marital': {
'Never Married': 0,
"Single": 0,
'Married': 0
},
'gender': {
'male': 0,
'female': 0
},
'location': {},
'race': {},
'language': {}
}
for patient in patients:
age = abs(datetime.today().year - patient.birth_date.year)
marital = str(patient.marital_status)
gender = str(patient.gender)
locations = [str(i.state) for i in patient.addresses]
race = str(patient.get_extension('us-core-race'))
languages = [str(i) for i in patient.communications.languages]
if age <= 9: statistics['age']['0 to 9'] += 1
elif age >= 10 and age <= 19: statistics['age']['10 to 19'] += 1
elif age >= 20 and age <= 29: statistics['age']['20 to 29'] += 1
elif age >= 30 and age <= 39: statistics['age']['30 to 39'] += 1
elif age >= 40 and age <= 49: statistics['age']['40 to 49'] += 1
elif age >= 50 and age <= 59: statistics['age']['50 to 59'] += 1
elif age >= 60 and age <= 69: statistics['age']['60 to 69'] += 1
elif age >= 70 and age <= 79: statistics['age']['70 to 79'] += 1
elif age >= 80 and age <= 100: statistics['age']['80 to 100'] += 1
else: statistics['age']['> 100'] += 1
statistics['marital'][marital] += 1
statistics['gender'][gender] += 1
for i in locations:
if statistics['location'].get(i) == None:
statistics['location'][i] = 1
else:
statistics['location'][i] += 1
if statistics['race'].get(race) == None: statistics['race'][race] = 1
else: statistics['race'][race] += 1
for i in languages:
if statistics['language'].get(i) == None:
statistics['language'][i] = 1
else:
statistics['language'][i] += 1
statistics['age'] = statistics['age'].values()
statistics['marital'] = statistics['marital'].values()
statistics['gender'] = statistics['gender'].values()
return render_template("demographic.html", stats=statistics)
def __init__(self):
self.pdf = canvas.Canvas("Record.pdf")
self.pdf.setTitle("Patient Record")
fhir = FHIR()
self.patients_lists = fhir.get_all_patients()
def save_doc(text, dropdown):
# Full list of patients
fhir = FHIR()
patients = fhir.get_all_patients()
document = Document()
new_line = " "
# Find number of patients
number_of_patients = 0
for i in patients:
number_of_patients = number_of_patients + 1
# Index of Patient
if dropdown == "Forename":
result = forename(text)
elif dropdown == "Surname":
result = surname(text)
else:
result = unique_identifier(text)
patient = patients[result]
# Address of Patient
address = patient.addresses[0]
communications = patient.communications
# Date Today
today = date.today()
# Marital Status
marital_status_patient = patient.marital_status.marital_status
# Age
age = today.year - patient.birth_date.year - (
(today.month, today.day) <
(patient.birth_date.month, patient.birth_date.day))
# Records of patients
patient_data = {"Unique Identifier": patient.uuid.upper(), "Surname": patient.name.family,
"Forename": patient.name.given, "Gender": patient.gender.capitalize(),
"Birthday": str(patient.birth_date.day) + "." + str(patient.birth_date.month) + "." \
+ str(patient.birth_date.year), "Age": str(age),
"Marital Status": str(marital_status_patient), "Address": address.full_address,
"Languages Spoken": communications.languages}
# Records of a patient
observations = fhir.get_patient_observations(
patient_data["Unique Identifier"].lower())
# Number of observations for 1 Patient
count = 0
for i in observations:
count = count + 1
# NHS Image
LOGO = "1280px-NHS-Logo.svg.png"
logo = document.add_picture(LOGO, width=Inches(1.50))
document.add_heading(patient.name.full_name, 1)
document.add_heading(new_line, 1)
# Adding general information about a patient in a word document
for data in patient_data:
p = document.add_paragraph()
p.add_run(data + ": ").bold = True
p.add_run(patient_data[data])
document.add_heading(new_line, 1)
# Creating a table
table = document.add_table(rows=1, cols=8)
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'Observation(s) '
hdr_cells[1].text = 'Number'
hdr_cells[2].text = 'Type'
hdr_cells[3].text = 'Status '
hdr_cells[5].text = 'Issued Date'
hdr_cells[6].text = 'Code'
hdr_cells[7].text = 'Result'
# Adding data into the table
for i in range(0, count):
row_cells = table.add_row().cells
row_cells[0].text = str(i + 1)
row_cells[1].text = observations[i].uuid.upper()
row_cells[2].text = observations[i].type.capitalize()
row_cells[3].text = observations[i].status.capitalize()
row_cells[5].text = str(observations[i].issued_datetime)
for component in observations[i].components:
row_cells[6].text = component.code
row_cells[7].text = component.display + ": " + component.quantity()
# Save the document
document.save(
str(patient.name.given[0]) + "." + str(patient.name.family) + "." +
str(age) + '.docx')
import matplotlib.pyplot as plt
from fhir_parser import FHIR
import math
fhir = FHIR()
patients = fhir.get_all_patients()
def getLanguageData():
languages = {}
for patient in patients:
for language in patient.communications.languages:
languages.update({language: languages.get(language, 0) + 1})
print(languages)
return languages
def getMaritalStatus():
maritalStatus = {}
for patient in patients:
maritalStatus.update({
str(patient.marital_status):
maritalStatus.get(str(patient.marital_status), 0) + 1
})
return maritalStatus
def getAge():
ages = {}
for patient in patients:
# print(math.floor(patient.age()))
def getSpecificPatient(uuid):
fhir = FHIR()
patients = fhir.get_all_patients()
specific_patient = fhir.get_patient(uuid)
return specific_patient