def clinical_tests():
other_tests = {
'USG Abdomen': ['Abnormal'],
'CECT Abdomen and Thorax': ['Visceral Metastasis'],
'PET Scan': ['Visceral Metastasis', 'Skeletal Metastasis'],
'Bone Scan': ['Skeletal Metastasis']
}
test_key = list(other_tests.keys())
data_list = []
for test in test_key:
test_name = test
test_done = gf.get_bool()
if test_done:
test_done = test_name + " done"
data_list.append(test_done)
abnormal = other_tests[test]
for diagnosis in abnormal:
test_diag = gf.get_bool()
if test_diag:
test_details = 'data_not_available'
else:
diagnosis = 'Normal'
test_details = "NA"
data_list.append(diagnosis)
data_list.append(test_details)
else:
test_done = test_name + "_not_done"
data_list.append(test_done)
abnormal = other_tests[test]
for i in range(2 * len(abnormal)):
i = 'NA'
data_list.append(i)
return data_list
def cancer_table(file_number):
table_cancer = "previous_cancer_history"
type_of_cancer_list = []
year_diagnosis_list = []
treat_all = []
type_all = []
duration_all = []
data_return = []
type_of_cancer = 'data_not_available'
type_of_cancer_list.append(type_of_cancer)
year_diagnosis = 'data_not_available'
year_diagnosis_list.append(year_diagnosis)
col = ("File_number, Type_Cancer, Year_diagnosis")
treat_list = []
type_list = []
duration_list = []
treated, type, duration = ("NA", ) * 3
for treatment in PatientInfo.previous_cancer_treatment:
treat = gf.get_bool()
if treat:
treat_list.append(treatment)
type_treat = 'data_not_available'
type_list.append(type_treat)
duration_treat = 'data_not_available'
duration_list.append(duration_treat)
data = treatment, type_treat, duration_treat
columns = [
treatment, ("type_" + treatment), ("duration_" + treatment)
]
treated = "; ".join(treat_list)
type = "; ".join(type_list)
duration = "; ".join(duration_list)
elif not treat:
index_no = "No " + treatment
type_treat, duration_treat = ("NA", ) * 2
data = index_no, type_treat, duration_treat
columns = [
treatment, ("type_" + treatment), ("duration_" + treatment)
]
treat_all.append(treated)
type_all.append(type)
duration_all.append(duration)
all_data = [
type_of_cancer_list, year_diagnosis_list, treat_all, type_all,
duration_all
]
for index in all_data:
data_joint = "|".join(index)
data_return.append(data_joint)
return tuple(data_return)
def family_details(file_number, age_mother):
module_name = "family_details"
col_list = pccm_names.names_info(module_name)
marital_status = 'data_not_available'
siblings = gf.get_yes_no_na()
if siblings == 'yes':
siblings_number = gf.get_number(5)
sisters = gf.get_number(siblings_number)
brothers = siblings_number - sisters
elif siblings == 'no':
siblings_number, sisters, brothers = "No Siblings", "0", "0"
else:
siblings_number, sisters, brothers = (siblings, ) * 3
children_y_n = gf.get_yes_no_na()
if children_y_n == 'yes':
children_number = gf.get_number(5)
daughters = gf.get_number(children_number)
sons = children_number - daughters
elif children_y_n == 'no':
children_number, daughters, sons = 0, 0, 0
else:
children_number, daughters, sons = (children_y_n, ) * 3
menarche = gf.get_number_lt(11, 20)
menopause = gf.get_choice(PatientInfo.menopause_status)
menopause_age = menopause
if menopause == "Post-Menopausal":
menopause_age = gf.get_number_lt(menarche, 60)
lmp = "Last menstrual period " + str(menopause_age) + " yrs"
else:
lmp = gf.gen_date(base_date='2020-01-01')
# category = "Type of Period"
options = ["Regular", "Irregular", "Other"]
period_type = gf.get_choice(options)
number_pregnancy = gf.get_number(8)
if number_pregnancy == "0":
age_first_preg = 'No children'
age_last_preg = 'No children'
number_term = 'No children'
number_abortion = 'No children'
age_first = 'No children'
age_last = 'No children'
twice_birth = 'No children'
breast_feeding_data = 'No children'
kid_feeding = 'No children'
duration_feeding = 'No children'
breast_usage = 'No children'
elif number_pregnancy == "Requires Follow-up":
age_first_preg = number_pregnancy
age_last_preg = number_pregnancy
number_term = number_pregnancy
number_abortion = number_pregnancy
age_first = number_pregnancy
age_last = number_pregnancy
twice_birth = number_pregnancy
breast_feeding_data = number_pregnancy
kid_feeding = number_pregnancy
duration_feeding = number_pregnancy
breast_usage = number_pregnancy
else:
number_term = gf.get_number(5)
number_abortion = gf.get_number(5)
age_first_preg = gf.get_number_lt(menarche, 55)
age_last_preg = "NA"
age_first = gf.get_number_lt(0, age_mother)
age_last = age_first
if children_number == 0:
age_first = children_number
age_last = children_number
twice_birth = children_number
breast_feeding_data = children_number
kid_feeding = children_number
duration_feeding = children_number
breast_usage = children_number
elif children_y_n == 'yes':
age_first = gf.get_number_lt(0, age_mother)
if age_first_preg == "NA":
age_first_preg = int(age_mother) - int(age_first)
if int(children_number) > 1:
age_last = gf.get_number_lt(0, age_first_preg)
age_last_preg = age_mother - age_last
if age_last_preg == "NA":
age_last_preg = str(int(age_mother) - int(age_last))
twice_birth = gf.get_choice(
["Two births in a year", "No two births in a year"])
else:
age_last = age_first
age_last_preg = 'NA'
twice_birth = "NA"
breast_feeding = gf.get_bool()
if breast_feeding:
breast_feeding_data = "Breast feeding"
feed_details = bct.feed_duration(file_number, children_number)
else:
breast_feeding_data = "No Breast feeding"
feed_details = ("NA", ) * 3
kid_feeding, duration_feeding, breast_usage = feed_details
else:
age_first = str(children_y_n)
age_last = str(children_y_n)
twice_birth = str(children_y_n)
breast_feeding_data = str(children_y_n)
kid_feeding = str(children_y_n)
duration_feeding = str(children_y_n)
breast_usage = str(children_y_n)
fert_treat_y_n = gf.get_yes_no()
if fert_treat_y_n == 'yes':
fert_treat = "Fertility Treatment used"
type_fert = gf.get_choice(PatientInfo.fert_treat)
detail_fert = 'data_not_available'
cycles_fert = gf.get_number_lt(1, 4)
success_fert = gf.get_choice(
["Pregnancy from Treatment", "No pregnancy from treatment"])
elif fert_treat_y_n == "no":
fert_treat = "No Fertility Treatment used"
type_fert = fert_treat
detail_fert = fert_treat
cycles_fert = fert_treat
success_fert = fert_treat
else:
fert_treat = str(fert_treat_y_n)
type_fert = str(fert_treat_y_n)
detail_fert = str(fert_treat_y_n)
cycles_fert = str(fert_treat_y_n)
success_fert = str(fert_treat_y_n)
birth_control = gf.get_yes_no_na()
if birth_control == 'yes':
type_birth_control = gf.get_choice(
["Birth control pills", "Copper-T", "Condom"])
detail_birth_control = 'data_not_available'
duration_birth_control = gf.get_number(max_no=age_mother)
elif birth_control == 'no':
type_birth_control = "No birth control used"
detail_birth_control = "No birth control used"
duration_birth_control = "No birth control used" * 3
else:
type_birth_control = birth_control
detail_birth_control = birth_control
duration_birth_control = birth_control
data_list = [
marital_status, siblings_number, sisters, brothers, children_number,
daughters, sons, menarche, menopause, menopause_age, lmp, period_type,
number_pregnancy, number_term, number_abortion, age_first,
age_first_preg, age_last, age_last_preg, twice_birth,
breast_feeding_data, kid_feeding, duration_feeding, breast_usage,
fert_treat, type_fert, detail_fert, cycles_fert, success_fert,
type_birth_control, detail_birth_control, duration_birth_control
]
return tuple(data_list)
def report(self):
data_list = ask.default_data_list(self.col_list_all[1:])
check_mammo = gf.get_choice(MultiTest.test_reason)
reason_report = check_mammo
mammo = gf.get_bool()
if mammo:
tomo = gf.get_bool()
if tomo:
tomography_y_n = "Yes"
else:
tomography_y_n = "No"
report_date = gf.gen_date('2019-03-13')
mammo_place = gf.get_choice(["PCCM", "Outside"])
if mammo_place == "Outside":
mammography_place = names.get_full_name()
else:
mammography_place = mammo_place
mammography_indication = gf.get_choice(Radiology.mammo_indication)
mammography_breast = gf.get_choice(MultiTest.breast_cancer)
mammo_mass = gf.get_bool()
mass_all = []
mass_data = ['no_mass_detected']
mass_all.append(mass_data)
mammography_massnumber = "No mass detected"
mammography_masslocation = "No mass detected"
mammography_massshape = "No mass detected"
mammography_massmargin = "No mass detected"
mammography_massnipple_cm = "No mass detected"
mammography_masssize = "No mass detected"
mammography_masssize_unit = "No mass detected"
mass_longest_dimension = 'na'
if mammo_mass:
mass_breast = gf.get_choice(MultiTest.breast_cancer)
mass = radio_tables.MassCalcification(self.table, mass_breast,
self.file_number,
self.user_name)
mammography_massnumber, mass_dat = mass.multiple_mass()
[
mammography_masslocation, mass_name, mass_quadrant,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mass_longest_dimension, mammography_masssize_unit, modality
] = mass_dat[1:]
mammography_calcificationnumber = "No Calcification detected"
mammography_calcificationlocation = "No Calcification detected"
mammography_calcificationtype = "No Calcification detected"
mammography_calcification_comments = "No Calcification detected"
calc = gf.get_bool()
if calc:
mammography_calcification = radio_tables.cal_table(
self.file_number, mammography_breast)
(mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments
) = mammography_calcification
mammography_skin_involvement = gf.get_choice(
Radiology.skin_involvement)
mammography_node_description = 'nodes_not_described'
mammography_node_size = 'nodes_not_described'
mammography_node_size_unit = 'nodes_not_described'
node_description = gf.get_bool()
if node_description:
mammography_node_description = gf.get_choice(
Radiology.node_description)
mammography_node_size = gf.get_number_lt(3, 10)
mammography_node_size_unit = 'cm'
mammo_birad = gf.get_bool()
if mammo_birad:
mammography_birad = radio_tables.birads()
else:
mammography_birad = "BI-RAD not assigned in report"
mammography_impression = gf.get_choice(Radiology.impression)
data_list = [
reason_report, report_date, mammography_place,
mammography_indication, mammography_breast,
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize[0],
mammography_masssize_unit, mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments,
mammography_skin_involvement, mammography_node_description,
mammography_node_size, mammography_node_size_unit,
mammography_birad, mammography_impression, tomography_y_n
]
data_list = [str(dat) for dat in data_list]
data_list = [self.file_number] + data_list + [
self.user_name, sql.last_update()
]
return data_list
def breast_symptoms(file_number, user_name):
module_name = "breast_symptoms"
symp_present = gf.get_bool()
if symp_present:
rb_symp_list = []
rb_dur_list = []
lb_symp_list = []
lb_dur_list = []
for index in PatientInfo.symp_state:
symp = gf.get_bool()
if symp:
RB = gf.get_bool()
if RB:
rb_symp = index
rb_dur = str(gf.get_number(5)) + gf.get_choice(
['weeks', 'year', 'months', 'days'])
rb_symp_list.append(rb_symp)
rb_dur_list.append(rb_dur)
LB = gf.get_bool()
if LB:
lb_symp = index
lb_dur = str(gf.get_number(5)) + gf.get_choice(
['weeks', 'year', 'months', 'days'])
lb_symp_list.append(lb_symp)
lb_dur_list.append(lb_dur)
rb_symps = "; ".join(rb_symp_list)
rb_duration = "; ".join(rb_dur_list)
lb_symps = "; ".join(lb_symp_list)
lb_duration = "; ".join(lb_dur_list)
data_list_symp = [rb_symps, rb_duration, lb_symps, lb_duration]
for index in range(0, len(data_list_symp)):
if data_list_symp[index] == '':
data_list_symp[index] = "NA"
rb_symp_list = []
rb_dur_list = []
lb_symp_list = []
lb_dur_list = []
rb_symps_other = "; ".join(rb_symp_list)
rb_duration_other = "; ".join(rb_dur_list)
lb_symps_other = "; ".join(lb_symp_list)
lb_duration_other = "; ".join(lb_dur_list)
data_list_other = [
rb_symps_other, rb_duration_other, lb_symps_other,
lb_duration_other
]
for index in range(0, len(data_list_other)):
if data_list_other[index] == '':
data_list_other[index] = "NA"
met = []
for symptom in PatientInfo.met_symptoms:
met_symp = gf.get_bool()
if met_symp:
met.append([symptom])
met_flat = [item for sublist in met for item in sublist]
data_met = "; ".join(met_flat)
if met_flat == []:
data_met = "No Metastatis Symptoms"
else:
data_list_other = [
"Symptoms at presentation not in report",
] * 4
data_list_symp = [
"Symptoms at presentation not in report",
] * 4
data_met = "Symptoms at presentation not in report"
columns_list = pccm_names.names_info(module_name)
data_list = data_list_symp + data_list_other + [data_met]
return tuple(data_list)