Example #1
0
def create_outcomes_tables(POP):
    OUTCOMES = POP[POP['SUBJECT_ICUSTAY_SEQ'] == 1]
    OUTCOMES = OUTCOMES[[
        'SUBJECT_ID', 'ICUSTAY_ID', 'ICUSTAY_INTIME', 'ICUSTAY_OUTTIME',
        'HOSPITAL_EXPIRE_FLG', 'DOD', 'ICUSTAY_ADMIT_AGE', 'GENDER',
        'SAPSI_FIRST', 'WEIGHT_FIRST', 'SOFA_FIRST', 'ICUSTAY_FIRST_SERVICE',
        'PACEMAKER', 'RISKFALLS'
    ]]

    # Conservative assumption : if no data, we assume they didn't die.
    OUTCOMES['HOSPITAL_EXPIRE_FLG'].fillna('N', inplace=True)
    # For Hospital expire flag, we need to replace the values by hand because we care what order.
    OUTCOMES['HOSPITAL_EXPIRE_FLG'].replace('N', 0, inplace=True)
    OUTCOMES['HOSPITAL_EXPIRE_FLG'].replace('Y', 1, inplace=True)

    # Change the columns to be categories, not strings
    OUTCOMES['ICUSTAY_FIRST_SERVICE'] = process.discretize_categories(
        OUTCOMES['ICUSTAY_FIRST_SERVICE'])
    OUTCOMES['GENDER'] = process.discretize_categories(OUTCOMES['GENDER'])
    OUTCOMES['PACEMAKER'] = process.discretize_categories(
        OUTCOMES['PACEMAKER'])
    OUTCOMES['RISKFALLS'] = process.discretize_categories(
        OUTCOMES['RISKFALLS'])
    # Add BMI to the table
    OUTCOMES['BMI'] = POP['WEIGHT_FIRST'] / (POP['HEIGHT'] * POP['HEIGHT'])
    return OUTCOMES
def create_demographics_tables(POP):
	# Do some extra constraining on the POP set
	DEMOGRAPHIC = POP[POP['SUBJECT_ICUSTAY_SEQ'] == 1]
	DEMOGRAPHIC = DEMOGRAPHIC[['SUBJECT_ID', 'ICUSTAY_ID', 'ICUSTAY_INTIME', 'ICUSTAY_OUTTIME', 'ICUSTAY_ADMIT_AGE', 'GENDER', 'SAPSI_FIRST', 'WEIGHT_FIRST', 'SOFA_FIRST', 'ICUSTAY_FIRST_SERVICE', 'PACEMAKER', 'RISKFALLS']]
	# Change the columns to be categories, not strings
	DEMOGRAPHIC['ICUSTAY_FIRST_SERVICE'] = process.discretize_categories(DEMOGRAPHIC['ICUSTAY_FIRST_SERVICE'])
	DEMOGRAPHIC['GENDER'] = process.discretize_categories(DEMOGRAPHIC['GENDER'])
	DEMOGRAPHIC['PACEMAKER'] = process.discretize_categories(DEMOGRAPHIC['PACEMAKER'])
	DEMOGRAPHIC['RISKFALLS'] = process.discretize_categories(DEMOGRAPHIC['RISKFALLS'])
	# Add BMI to the table
	DEMOGRAPHIC['BMI'] = POP['WEIGHT_FIRST'] / (POP['HEIGHT'] * POP['HEIGHT'])
	return DEMOGRAPHIC
def create_outcomes_tables(POP):
	OUTCOMES = POP[POP['SUBJECT_ICUSTAY_SEQ'] == 1]
	OUTCOMES = OUTCOMES[['SUBJECT_ID', 'ICUSTAY_ID', 'ICUSTAY_INTIME', 'ICUSTAY_OUTTIME', 'HOSPITAL_EXPIRE_FLG', 'DOD', 'ICUSTAY_ADMIT_AGE', 'GENDER', 'SAPSI_FIRST', 'WEIGHT_FIRST', 'SOFA_FIRST', 'ICUSTAY_FIRST_SERVICE', 'PACEMAKER', 'RISKFALLS']]

	# Conservative assumption : if no data, we assume they didn't die.
	OUTCOMES['HOSPITAL_EXPIRE_FLG'].fillna('N', inplace=True)
	# For Hospital expire flag, we need to replace the values by hand because we care what order.
	OUTCOMES['HOSPITAL_EXPIRE_FLG'].replace('N', 0, inplace=True)
	OUTCOMES['HOSPITAL_EXPIRE_FLG'].replace('Y', 1, inplace=True)

	# Change the columns to be categories, not strings
	OUTCOMES['ICUSTAY_FIRST_SERVICE'] = process.discretize_categories(OUTCOMES['ICUSTAY_FIRST_SERVICE'])
	OUTCOMES['GENDER'] = process.discretize_categories(OUTCOMES['GENDER'])
	OUTCOMES['PACEMAKER'] = process.discretize_categories(OUTCOMES['PACEMAKER'])
	OUTCOMES['RISKFALLS'] = process.discretize_categories(OUTCOMES['RISKFALLS'])
	# Add BMI to the table
	OUTCOMES['BMI'] = POP['WEIGHT_FIRST'] / (POP['HEIGHT'] * POP['HEIGHT'])
	return OUTCOMES
Example #4
0
def create_demographics_tables(POP):
    # Do some extra constraining on the POP set
    DEMOGRAPHIC = POP[POP['SUBJECT_ICUSTAY_SEQ'] == 1]
    DEMOGRAPHIC = DEMOGRAPHIC[[
        'SUBJECT_ID', 'ICUSTAY_ID', 'ICUSTAY_INTIME', 'ICUSTAY_OUTTIME',
        'ICUSTAY_ADMIT_AGE', 'GENDER', 'SAPSI_FIRST', 'WEIGHT_FIRST',
        'SOFA_FIRST', 'ICUSTAY_FIRST_SERVICE', 'PACEMAKER', 'RISKFALLS'
    ]]
    # Change the columns to be categories, not strings
    DEMOGRAPHIC['ICUSTAY_FIRST_SERVICE'] = process.discretize_categories(
        DEMOGRAPHIC['ICUSTAY_FIRST_SERVICE'])
    DEMOGRAPHIC['GENDER'] = process.discretize_categories(
        DEMOGRAPHIC['GENDER'])
    DEMOGRAPHIC['PACEMAKER'] = process.discretize_categories(
        DEMOGRAPHIC['PACEMAKER'])
    DEMOGRAPHIC['RISKFALLS'] = process.discretize_categories(
        DEMOGRAPHIC['RISKFALLS'])
    # Add BMI to the table
    DEMOGRAPHIC['BMI'] = POP['WEIGHT_FIRST'] / (POP['HEIGHT'] * POP['HEIGHT'])
    return DEMOGRAPHIC