def _transform(self, tables):
#group services by hadm_id and compute whether first service per hadm_id is a surgery
services = tables['services']
curr_service_cats = pd.Series(services['curr_service'].cat.categories)
surgery_cats = curr_service_cats[curr_service_cats.str.contains('SURG')]
surgery_codes = surgery_cats.index
services['curr_service_code'] = services['curr_service'].cat.codes
services.sort_index(by=['hadm_id', 'transfertime'], inplace=True)
first_services = services.groupby('hadm_id', sort=False)['curr_service_code'].first().reset_index()
first_services['is_surgery'] = first_services['curr_service_code'].isin(surgery_codes)
#get the other tables
icu = tables['icustayevents']
adm = tables['admissions']
pat = tables['patients']
#assign_prefixes to all of them before the join
df_prefix = [
(first_services, 's'),
(icu, 'icu'),
(adm, 'adm'),
(pat, 'pat')
]
for df, prefix in df_prefix:
df.columns = [prefix + '.' + col for col in df.columns]
j_df = left_join(icu, adm, left_on='icu.hadm_id', right_on='adm.hadm_id')
j_df = left_join(j_df, pat, left_on='icu.subject_id', right_on='pat.subject_id')
j_df = left_join(j_df, first_services, left_on='icu.hadm_id', right_on='s.hadm_id')
df = pd.DataFrame(index=j_df.index)
df['icustay_id'] = j_df['icu.icustay_id']
df['pre_icu_los'] = j_df['icu.intime'] - j_df['adm.admittime']
df['intime'] = j_df['icu.intime']
df['outtime'] = j_df['icu.outtime']
df['deathtime'] = j_df['adm.deathtime']
df['age'] = j_df['icu.intime'] - j_df['pat.dob']
df['elective_surgery'] = (j_df['adm.admission_type'].isin(['ELECTIVE'])) & j_df['s.is_surgery']
df['emergency_surgery'] = (j_df['adm.admission_type'].isin(['EMERGENCY', 'URGENT'])) & j_df['s.is_surgery']
df['age_group'] = 1
df.loc[df['age'] / np.timedelta64(1, 'M') <= 1, 'age_group'] = 0
df.loc[df['age'] / np.timedelta64(1, 'M') > 16, 'age_group'] = 2
df['age_group'] = pd.Categorical.from_codes(df['age_group'], categories=['neonate', 'middle', 'adult'])
df['diagnosis'] = j_df['adm.diagnosis']
df['icustay_expire_flag'] = j_df['adm.deathtime'] <= j_df['icu.outtime'] # covers when deathtime is before, due to typographical errors
df['icustay_expire_flag'] |= (j_df['adm.dischtime'] <= j_df['icu.outtime']) & j_df['adm.discharge_location'].isin(['DEAD/EXPIRED'])
df['hospital_expire_flag'] = j_df['adm.discharge_location'].isin(['DEAD/EXPIRED'])
return df
def _transform(self, tables):
pat = tables['patients']
adm = tables['admissions']
icu = tables['icustayevents']
if self.subject_ids is not None:
pat = pat[pat['subject_id'].isin(self.subject_ids)]
adm = adm[adm['subject_id'].isin(self.subject_ids)]
icu = icu[icu['subject_id'].isin(self.subject_ids)]
if self.hadm_ids is not None:
adm = adm[adm['hadm_id'].isin(self.hadm_ids)]
icu = icu[icu['hadm_id'].isin(self.hadm_ids)]
if self.icustay_ids is not None:
icu = icu[icu['icustay_id'].isin(self.icustay_ids)]
df = left_join(pat, adm, 'subject_id')
df['hadm_death'] = df['deathtime'].notnull()
df['time_of_death'] = df['dod_hosp'].fillna(df['dod_ssn'])
df['time_until_death'] = df['time_of_death'] - df['dischtime']
df = AdmICUStayMapper().transform({
'icustayevents': icu,
'admissions': df
})
df['icustay_death'] = (df['deathtime'] >= df['intime']) & (df['deathtime'] <= df['outtime'])
return df[['subject_id', 'hadm_id', 'icustay_id', 'icustay_death', 'hadm_death', 'time_of_death', 'time_until_death']]
def _transform(self, tables):
pat = tables['patients']
adm = tables['admissions']
icu = tables['icustayevents']
if self.subject_ids is not None:
pat = pat[pat['subject_id'].isin(self.subject_ids)]
adm = adm[adm['subject_id'].isin(self.subject_ids)]
icu = icu[icu['subject_id'].isin(self.subject_ids)]
if self.hadm_ids is not None:
adm = adm[adm['hadm_id'].isin(self.hadm_ids)]
icu = icu[icu['hadm_id'].isin(self.hadm_ids)]
if self.icustay_ids is not None:
icu = icu[icu['icustay_id'].isin(self.icustay_ids)]
df = left_join(pat, adm, 'subject_id')
df['hadm_death'] = df['deathtime'].notnull()
df['time_of_death'] = df['dod_hosp'].fillna(df['dod_ssn'])
df['time_until_death'] = df['time_of_death'] - df['dischtime']
df = AdmICUStayMapper().transform({
'icustayevents': icu,
'admissions': df
})
df['icustay_death'] = (df['deathtime'] >= df['intime']) & (
df['deathtime'] <= df['outtime'])
return df[[
'subject_id', 'hadm_id', 'icustay_id', 'icustay_death',
'hadm_death', 'time_of_death', 'time_until_death'
]]
def _transform(self, tables):
labevents = tables['labevents']
if self.lab_item_ids is not None:
labevents = labevents[labevents['itemid'].isin(self.lab_item_ids)]
icustayevents = tables['icustayevents']
df = left_join(
labevents[['hadm_id', 'charttime']],
icustayevents[['hadm_id', 'icustay_id', 'intime', 'outtime']],
'hadm_id')
df = df[(df['charttime'] >= df['intime']) & (df['charttime'] <= df['outtime'])]
icustay_ids = df['icustay_id'].unique()
return icustayevents[icustayevents['icustay_id'].isin(icustay_ids)]
def _transform(self, tables):
labevents = tables['labevents']
if self.lab_item_ids is not None:
labevents = labevents[labevents['itemid'].isin(self.lab_item_ids)]
icustayevents = tables['icustayevents']
df = left_join(
labevents[['hadm_id', 'charttime']],
icustayevents[['hadm_id', 'icustay_id', 'intime',
'outtime']], 'hadm_id')
df = df[(df['charttime'] >= df['intime'])
& (df['charttime'] <= df['outtime'])]
icustay_ids = df['icustay_id'].unique()
return icustayevents[icustayevents['icustay_id'].isin(icustay_ids)]
def _transform(self, tables):
#group services by hadm_id and compute whether first service per hadm_id is a surgery
services = tables['services']
curr_service_cats = pd.Series(services['curr_service'].cat.categories)
surgery_cats = curr_service_cats[curr_service_cats.str.contains(
'SURG')]
surgery_codes = surgery_cats.index
services['curr_service_code'] = services['curr_service'].cat.codes
services.sort_index(by=['hadm_id', 'transfertime'], inplace=True)
first_services = services.groupby(
'hadm_id', sort=False)['curr_service_code'].first().reset_index()
first_services['is_surgery'] = first_services[
'curr_service_code'].isin(surgery_codes)
#get the other tables
icu = tables['icustayevents']
adm = tables['admissions']
pat = tables['patients']
#assign_prefixes to all of them before the join
df_prefix = [(first_services, 's'), (icu, 'icu'), (adm, 'adm'),
(pat, 'pat')]
for df, prefix in df_prefix:
df.columns = [prefix + '.' + col for col in df.columns]
j_df = left_join(icu,
adm,
left_on='icu.hadm_id',
right_on='adm.hadm_id')
j_df = left_join(j_df,
pat,
left_on='icu.subject_id',
right_on='pat.subject_id')
j_df = left_join(j_df,
first_services,
left_on='icu.hadm_id',
right_on='s.hadm_id')
df = pd.DataFrame(index=j_df.index)
df['icustay_id'] = j_df['icu.icustay_id']
df['pre_icu_los'] = j_df['icu.intime'] - j_df['adm.admittime']
df['intime'] = j_df['icu.intime']
df['outtime'] = j_df['icu.outtime']
df['deathtime'] = j_df['adm.deathtime']
df['age'] = j_df['icu.intime'] - j_df['pat.dob']
df['elective_surgery'] = (j_df['adm.admission_type'].isin(
['ELECTIVE'])) & j_df['s.is_surgery']
df['emergency_surgery'] = (j_df['adm.admission_type'].isin(
['EMERGENCY', 'URGENT'])) & j_df['s.is_surgery']
df['age_group'] = 1
df.loc[df['age'] / np.timedelta64(1, 'M') <= 1, 'age_group'] = 0
df.loc[df['age'] / np.timedelta64(1, 'M') > 16, 'age_group'] = 2
df['age_group'] = pd.Categorical.from_codes(
df['age_group'], categories=['neonate', 'middle', 'adult'])
df['diagnosis'] = j_df['adm.diagnosis']
df['icustay_expire_flag'] = j_df['adm.deathtime'] <= j_df[
'icu.outtime'] # covers when deathtime is before, due to typographical errors
df['icustay_expire_flag'] |= (
j_df['adm.dischtime'] <= j_df['icu.outtime']
) & j_df['adm.discharge_location'].isin(['DEAD/EXPIRED'])
df['hospital_expire_flag'] = j_df['adm.discharge_location'].isin(
['DEAD/EXPIRED'])
return df
def _transform(self, tables):
ie = tables['icustayevents']
adm = tables['admissions']
df = left_join(ie, adm, ['hadm_id', 'subject_id'])
return df
def _transform(self, tables):
ie = tables['icustayevents']
adm = tables['admissions']
df = left_join(ie, adm, ['hadm_id', 'subject_id'])
return df
