else:
lab_result_status = 'resulted'
delta_hours = get_hours_between_datetimes(
pcr_sample_times[str(patient_mrn)], str(lab_sample_time))
if delta_hours > -48 and delta_hours < 7 * 24:
mapped_lab_name = map_lab_name(lab_name)
mapped_lab_sample_site = map_lab_sample_site(lab_name, lab_sample_site)
try:
mapped_lab_value = map_lab_result_value(lab_result_string)
except:
print('Invalid lab value: ' + str(lab_result_string))
continue
if mapped_lab_name == 'venous_o2_sat':
print(mapped_lab_value)
lab_data_rows.append([
patient_mrn, mapped_lab_name, mapped_lab_sample_site,
map_time(lab_sample_time),
map_time(lab_result_time), lab_result_status, lab_result_units,
lab_result_string, mapped_lab_value
])
print('Total rows: %d' % len(lab_data_rows))
write_csv(TABLE_COLUMNS['lab_data'], lab_data_rows,
os.path.join(CSV_DIRECTORY, 'lab_data.csv'))
patient_mrn = str(row[0])
patient_ramq = str(row[1])
patient_age = row[-1]
patient_birth_sex = row[-2]
try:
patient_covid_status = map_patient_covid_status(row[-4])
except:
print('Invalid COVID status: ' + str(row[-4]))
continue
pcr_result_time = row[-6]
pcr_sample_time = row[-7]
if map_time(pcr_sample_time) == '':
print('Skipping row: invalid PCR time')
continue
if patient_mrn not in patient_covid_statuses:
patient_covid_statuses[patient_mrn] = [patient_covid_status]
else:
patient_covid_statuses[patient_mrn].append(patient_covid_status)
if patient_mrn not in patient_data:
patient_data[patient_mrn] = []
patient_data[patient_mrn].append([
patient_mrn,
map_patient_ramq(patient_ramq),
map_time(pcr_sample_time), LOCAL_SITE_CODE, patient_covid_status,
if hours < -48: continue
patients_with_imaging.append(row_patient_mrn)
imaging_accession_numbers.append(row.accession_number)
imaging_accession_uid = generate_accession_uid(row.accession_number)
imaging_acquired_time = row.date_heure_debut_examen
if 'rx' in lower_desc: modality = 'xr'
elif 'scan' in lower_desc: modality = 'ct'
imaging_data_rows.append([
row_patient_mrn,
imaging_accession_uid,
modality, 'chest',
map_time(imaging_acquired_time)
])
patients_with_imaging = np.unique(patients_with_imaging)
imaging_accession_numbers = np.unique(imaging_accession_numbers)
print('Number of patients with imaging: %d' % \
len(patients_with_imaging))
write_csv(['accession_number'], \
[[x] for x in imaging_accession_numbers], \
IMAGING_LIST_FILENAME)
write_csv(TABLE_COLUMNS['imaging_data'], imaging_data_rows,
os.path.join(CSV_DIRECTORY, 'imaging_data.csv'))
pcr_sample_times[patient_mrn].append(pcr_time)
# Get ER visit data from ADT
df = sql_query(
"SELECT dossier, noadm, dhreadm, dhredep, diagdesc FROM " +
"dw_test.orcl_cichum_sejurg_live WHERE " + \
"dossier in ('" + "', '".join(patient_mrns) + "') " + \
"AND dhreadm > '2020-01-01'"
)
episode_data_rows = [
[
map_string_lower(row.dossier),
map_string_lower(int(row.noadm)),
map_episode_unit_type('ER', None),
map_time(row.dhreadm),
map_time(row.dhredep),
map_string_lower(row.diagdesc),
get_hours_between_datetimes( \
row.dhreadm, row.dhredep, default_now=True),
] for i, row in df.iterrows() \
if row.dhreadm != '' and \
pcr_sample_times[str(row.dossier)][0] != '' and \
get_hours_between_datetimes( \
row.dhreadm, pcr_sample_times[str(row.dossier)][0]
) > 0
]
episode_ids = [
[map_string_lower(row.dossier)]
for i, row in df.iterrows()
if 'annul' in str(pcr_result_value) or \
pcr_result_value is None or \
map_pcr_result_value(pcr_result_value) == '':
continue
if patient_mrn not in pcr_sample_times:
pcr_sample_times[patient_mrn] = []
pcr_sample_times[patient_mrn].append(str(pcr_sample_time))
pcr_data_rows.append([
patient_mrn,
map_pcr_name(pcr_name),
map_pcr_sample_site(pcr_name, pcr_sample_site),
map_time(pcr_sample_time),
map_time(pcr_result_time),
map_pcr_result_value(pcr_result_value),
map_pcr_result_status(pcr_result_value)
])
live_sheet_rows = read_csv(LIVE_SHEET_FILENAME, remove_duplicates=True)
for row in live_sheet_rows:
is_external = (row[-3] == 'External')
if is_external: continue
patient_mrn = str(row[0])
if patient_mrn in patient_mrns:
#print(df.size)
for index, row in df.iterrows():
patient_mrn = str(row.dossier)
observation_name = row.servacro
observation_value = row.rsltvalue
observation_time = row.startdtm
observation_unit = row.unitcd
try:
mapped_observation_name = map_observation_name(observation_name,
observation_unit)
except:
continue
mapped_observation_time = map_time(observation_time)
mapped_observation_value = map_float_value(observation_value)
delta_hours = get_hours_between_datetimes(
pcr_sample_times[str(patient_mrn)], str(observation_time))
9
if delta_hours < -48: continue
observation_data_rows.append([
patient_mrn, mapped_observation_name, mapped_observation_time,
mapped_observation_value
])
print('Done fetching observations from Oacis')
df = sql_query(
for index, row in df.iterrows():
if row.longdesc.lower() in DRUG_SKIP_VALUES:
continue
patient_mrn = row.dossier
drug_name = map_drug_name(row.longdesc)
if 'ou placebo' in drug_name:
continue
drug_start_time = row.orderstartdtm
drug_end_time = row.orderstopdtm
delta_hours = get_hours_between_datetimes(
pcr_sample_times[str(patient_mrn)], str(drug_start_time))
if delta_hours < -48: continue
drug_data_rows.append([
patient_mrn, drug_name,
map_time(drug_start_time),
map_time(drug_end_time),
map_drug_frequency(row.intervalsig),
map_drug_route(row.routecd)
])
print('Total rows: %d' % len(drug_data_rows))
write_csv(TABLE_COLUMNS['drug_data'], drug_data_rows,
os.path.join(CSV_DIRECTORY, 'drug_data.csv'))