File size: 6,133 Bytes
53a6def | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | """
Find information on COPD, respiratory, rescue med and death event tracking
for patients within a timeframe
"""
import json
import pandas as pd
import numpy as np
merged_cols = ['adm_per_year', 'copd_resp_per_year',
'anxiety_depression_per_year',
'rescue_meds_per_year', 'anxiety_depression_presc_per_year']
base_cols = ['admission_any', 'admission_copd_resp',
'admission_anxiety_depression',
'presc_rescue_med', 'presc_anxiety_depression']
n_cols = ["n_" + col for col in base_cols]
adm_cols = ['SafeHavenID', 'ADMDATE', 'admission_any', 'admission_copd_resp']
presc_cols = ['SafeHavenID', 'PRESC_DATE', 'rescue_meds']
def read_deaths(extract_data_path):
"""
Read in deaths dataset
--------
:param extract_data_path: path to data extracts
:return: dataframe
"""
filename = extract_data_path + 'Deaths_Cohort3R.csv'
cols = ['SafeHavenID', 'DOD']
df = pd.read_csv(filename, usecols=cols).drop_duplicates()
df['DOD'] = pd.to_datetime(df.DOD)
return df
def filter_data(df, date_col, eoy_date, start_date, end_date, typ):
"""
Filter data to only include events occurring within given date range
--------
:param df: dataframe
:param date_col: str name of date column
:param eoy_date: end of year date
:param start_date: validation data start date
:param end_date: validation data end date
:param typ: type of data: 'adm', 'presc', 'merged', 'deaths'
:return: filtered dataframe
"""
if typ == 'merged':
df = df[df.eoy == eoy_date]
else:
df = df[(df[date_col] >= start_date) & (df[date_col] < end_date)]
return df
def calc_time_to_event(df, date_col, start_date, new_col):
"""
Calculate time to next event
--------
:param df: dataframe
:param date_col: str name of date column
:param start_date: validation data start date
:param new_col: new column name
:return: dataframe with SafeHavenID days to event
"""
df_next = df.groupby('SafeHavenID').agg(next_event=(date_col, min))
df_next = (df_next - start_date) / np.timedelta64(1, 'M')
df_next.columns = ['time_to_' + new_col]
return df_next
def bucket_time_to_event(df):
"""
Calculate time in months to next event and bucket into
1, 3, 6, 12, 12+ months.
--------
:param df: dataframe
:return: dataframe with event times in categories
"""
month = [-1, 1, 3, 6, 12, 13]
label = ['1', '3', '6', '12', '12+']
df = df.apply(lambda x: pd.cut(x, month, labels=label))
df = df.fillna('12+')
return df
def calculate_event_metrics(data_path, eoy_date, start_date, end_date):
"""
Generate tables with number of events in 12 months and
boolean for events
--------
:param data_path: path to generated data
:param eoy_date: end of year date
:param start_date: validation data start date
:param end_date: validation data end date
"""
# Load in data
merged = pd.read_pickle(data_path + 'merged.pkl')
# Select relevant dates and columns
merged = filter_data(
merged, 'eoy', eoy_date, start_date, end_date, 'merged')
df_event = merged[['SafeHavenID'] + merged_cols]
# Create frame with total events within 12mo period
df_count = df_event.copy()
df_count.columns = ['SafeHavenID'] + n_cols
df_count.to_pickle(data_path + 'metric_table_counts.pkl')
# Create frame with boolean events within 12mo period
df_event[merged_cols] = (df_event[merged_cols] > 0).astype(int)
df_event.columns = ['SafeHavenID'] + base_cols
df_event.to_pickle(data_path + 'metric_table_events.pkl')
def calculate_next_event(data_path, extract_data_path, eoy_date,
start_date, end_date):
"""
Generate table with the time in 1, 3, 6, 12, 12+ months
--------
:param data_path: path to generated data
:param extract_data_path: path to data extracts
:param eoy_date: end of year date
:param start_date: validation data start date
:param end_date: validation data end date
"""
# Find next adm events
adm = pd.read_pickle(data_path + 'validation_adm_proc.pkl')
adm = filter_data(
adm, 'ADMDATE', eoy_date, start_date, end_date, 'adm')
adm['admission_any'] = 1
adm['admission_copd_resp'] = adm.copd_event | adm.resp_event
adm = adm[adm_cols]
time_to_adm_any = calc_time_to_event(
adm, 'ADMDATE', start_date, 'admission_any')
time_to_adm_copd = calc_time_to_event(
adm[adm.admission_copd_resp == 1], 'ADMDATE', start_date,
'admission_copd_resp')
# Find next presc events
presc = pd.read_pickle(data_path + 'validation_presc_proc.pkl')
presc = filter_data(
presc, 'PRESC_DATE', eoy_date, start_date, end_date, 'presc')
presc = presc[presc_cols]
presc = presc[presc.rescue_meds == 1]
time_to_rescue = calc_time_to_event(
presc, 'PRESC_DATE', start_date, 'presc_rescue_med')
# Find next deaths
deaths = read_deaths(extract_data_path)
deaths = filter_data(
deaths, 'DOD', eoy_date, start_date, end_date, 'deaths')
deaths['death'] = 1
time_to_death = calc_time_to_event(
deaths, 'DOD', start_date, 'death')
# Merge results
frames = [time_to_adm_any, time_to_adm_copd, time_to_rescue, time_to_death]
results = pd.concat(frames, join='outer', axis=1)
results = bucket_time_to_event(results)
results.to_pickle(data_path + 'metric_table_next.pkl')
def main():
# Load in config items
with open('../../../config.json') as json_config_file:
config = json.load(json_config_file)
data_path = config['model_data_path']
extract_data_path = config['extract_data_path']
eoy_date = pd.to_datetime(config['date'])
start_date = eoy_date + pd.Timedelta(days=1)
end_date = eoy_date + pd.offsets.DateOffset(years=1)
calculate_event_metrics(data_path, eoy_date, start_date, end_date)
calculate_next_event(data_path, extract_data_path, eoy_date,
start_date, end_date)
main()
|