def _transform(self, dfs):
icustay_index = dfs['icustay_detail'].set_index('icustay_id')
for df_name in ['chartevents', 'labevents', 'ioevents']:
df = dfs[df_name]
df['icustay_intime'] = icustay_index.loc[df['icustay_id'],
'icustay_intime'].values
df = df[df['charttime'] > df['icustay_intime']]
dfs[df_name] = df
demo_history = DemographicTransform().transform(dfs['icustay_detail'])
chart_history = ChartTransform().transform(dfs['chartevents'])
lab_history = LabTransform().transform(dfs['labevents'])
urine_history = UrineTransform().transform(dfs['ioevents'])
all_history = from_chunks(
[chart_history, lab_history, demo_history, urine_history])
all_history['charttime'] = big_dt_to_num(all_history['charttime'])
all_history = all_history.sort_index(
by=['subject_id', 'charttime']).reset_index(drop=True)
all_history['charttime'] = num_to_big_dt(all_history['charttime'])
all_history.drop_duplicates(inplace=True)
all_history = all_history[[
'subject_id', 'charttime', 'category', 'valuenum'
]]
return all_history
def _transform(self, icustay_detail):
df = icustay_detail
computed_df = pd.DataFrame(index=df.index)
computed_df['AGE'] = df['icustay_admit_age'].round()
computed_df['GENDER'] = df['gender'].cat.set_categories(['F', 'M'
]).cat.codes
computed_df['LOS'] = df['hospital_los'] / (60 * 24)
computed_df['SURVIVAL'] = pd.to_timedelta(df['dod'] -
df['icustay_intime']).dt.days
computed_df['WEIGHT'] = df['weight_first']
computed_df['HEIGHT'] = df['height'].round(decimals=1)
computed_df['SAPS'] = df['sapsi_first']
computed_df['SOFA'] = df['sofa_first']
computed_df.set_index(
[df['subject_id'],
big_dt_to_num(df['icustay_intime'])],
inplace=True)
stacked_ser = computed_df.stack(dropna=False)
stacked_df = stacked_ser.reset_index()
stacked_df.columns = [
'subject_id', 'charttime', 'category', 'valuenum'
]
stacked_df['charttime'] = num_to_big_dt(stacked_df['charttime'])
return stacked_df
def _transform(self, icustay_detail):
df = icustay_detail
computed_df = pd.DataFrame(index=df.index)
computed_df['AGE'] = df['icustay_admit_age'].round()
computed_df['GENDER'] = df['gender'].cat.set_categories(['F', 'M']).cat.codes
computed_df['LOS'] = df['hospital_los'] / (60 * 24)
computed_df['SURVIVAL'] = pd.to_timedelta(df['dod'] - df['icustay_intime']).dt.days
computed_df['WEIGHT'] = df['weight_first']
computed_df['HEIGHT'] = df['height'].round(decimals=1)
computed_df['SAPS'] = df['sapsi_first']
computed_df['SOFA'] = df['sofa_first']
computed_df.set_index([df['subject_id'], big_dt_to_num(df['icustay_intime'])], inplace=True)
stacked_ser = computed_df.stack(dropna=False)
stacked_df = stacked_ser.reset_index()
stacked_df.columns = ['subject_id', 'charttime', 'category', 'valuenum']
stacked_df['charttime'] = num_to_big_dt(stacked_df['charttime'])
return stacked_df
def _transform(self, dfs):
icustay_index = dfs['icustay_detail'].set_index('icustay_id')
for df_name in ['chartevents', 'labevents', 'ioevents']:
df = dfs[df_name]
df['icustay_intime'] = icustay_index.loc[df['icustay_id'], 'icustay_intime'].values
df = df[df['charttime'] > df['icustay_intime']]
dfs[df_name] = df
demo_history = DemographicTransform().transform(dfs['icustay_detail'])
chart_history = ChartTransform().transform(dfs['chartevents'])
lab_history = LabTransform().transform(dfs['labevents'])
urine_history = UrineTransform().transform(dfs['ioevents'])
all_history = from_chunks([chart_history, lab_history, demo_history, urine_history])
all_history['charttime'] = big_dt_to_num(all_history['charttime'])
all_history = all_history.sort_index(by=['subject_id', 'charttime']).reset_index(drop=True)
all_history['charttime'] = num_to_big_dt(all_history['charttime'])
all_history.drop_duplicates(inplace=True)
all_history = all_history[['subject_id', 'charttime', 'category', 'valuenum']]
return all_history
def _transform(self, dfs):
icustay_index = dfs["icustay_detail"].set_index("icustay_id")
for df_name in ["chartevents", "labevents", "ioevents"]:
df = dfs[df_name]
df["icustay_intime"] = icustay_index.loc[df["icustay_id"], "icustay_intime"].values
df = df[df["charttime"] > df["icustay_intime"]]
dfs[df_name] = df
demo_history = DemographicTransform().transform(dfs["icustay_detail"])
chart_history = ChartTransform().transform(dfs["chartevents"])
lab_history = LabTransform().transform(dfs["labevents"])
urine_history = UrineTransform().transform(dfs["ioevents"])
all_history = from_chunks([chart_history, lab_history, demo_history, urine_history])
all_history["charttime"] = big_dt_to_num(all_history["charttime"])
all_history = all_history.sort_index(by=["subject_id", "charttime"]).reset_index(drop=True)
all_history["charttime"] = num_to_big_dt(all_history["charttime"])
all_history.drop_duplicates(inplace=True)
all_history = all_history[["subject_id", "charttime", "category", "valuenum"]]
return all_history
def _transform(self, icustay_detail):
df = icustay_detail
computed_df = pd.DataFrame(index=df.index)
computed_df["AGE"] = df["icustay_admit_age"].round()
computed_df["GENDER"] = df["gender"].cat.set_categories(["F", "M"]).cat.codes
computed_df["LOS"] = df["los"] / (60 * 24)
computed_df["SURVIVAL"] = pd.to_timedelta(df["dod"] - df["icustay_intime"]).dt.days
computed_df["WEIGHT"] = df["weight_first"]
computed_df["HEIGHT"] = df["height"].round(decimals=1)
computed_df["SAPS"] = df["sapsi_first"]
computed_df["SOFA"] = df["sofa_first"]
computed_df.set_index([df["subject_id"], big_dt_to_num(df["icustay_intime"])], inplace=True)
stacked_ser = computed_df.stack(dropna=False)
stacked_df = stacked_ser.reset_index()
stacked_df.columns = ["subject_id", "charttime", "category", "valuenum"]
stacked_df["charttime"] = num_to_big_dt(stacked_df["charttime"])
return stacked_df
