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# Irregular sampling for PhysioNet-2012 dataset
# & Train/test/val splits
#
# Author: Theodoros Tsiligkaridis
# Last updated: May 4 2021
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# ## Irregular sampling
P_list = np.load('../processed_data/P_list.npy', allow_pickle=True)
arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)
ts_params = np.load('../processed_data/ts_params.npy', allow_pickle=True)
static_params = np.load('../processed_data/static_params.npy', allow_pickle=True)
print('number of samples: ', len(P_list))
print(len(ts_params), ts_params)
print(len(static_params), static_params)
# All patients were adults who were admitted for a wide variety of reasons to cardiac, medical, surgical, and trauma ICUs. ICU stays of less than 48 hours have been excluded.
# Up to 42 variables were recorded at least once during the first 48 hours after admission to the ICU. Not all variables are available in all cases, however.
# Six of these variables are general descriptors (collected on admission), and the remainder are time series, for which multiple observations may be available.
# Estimate max_len across dataset
n = len(P_list)
max_tmins = 48*60
len_ts = []
for ind in range(n): # for each patient
ts = P_list[ind]['ts']
unq_tmins = []
for sample in ts: # for each instance (time point)
current_tmin = sample[2]
if (current_tmin not in unq_tmins) and (current_tmin < max_tmins):
unq_tmins.append(current_tmin)
len_ts.append( len(unq_tmins))
print('max unique time series length:', np.max(len_ts)) # np.max(len_ts) = 214
# # Histogram of time points
# _ = plt.hist(np.array(len_ts), bins='auto')
# plt.xlabel('Number of time points')
# plt.ylabel('Counts')
# plt.show()
extended_static_list = ['Age', 'Gender=0', 'Gender=1', 'Height', 'ICUType=1', 'ICUType=2', 'ICUType=3', 'ICUType=4', 'Weight']
np.save('../processed_data/extended_static_params.npy', extended_static_list)
"""Group all patient time series into arrays"""
n = len(P_list)
max_len = 215
F = len(ts_params)
PTdict_list = []
max_hr = 0
for ind in range(n):
ID = P_list[ind]['id']
static = P_list[ind]['static']
ts = P_list[ind]['ts']
# find unique times
unq_tmins = []
for sample in ts:
current_tmin = sample[2]
if (current_tmin not in unq_tmins) and (current_tmin < max_tmins):
unq_tmins.append(current_tmin)
# print('unique times (mins):', unq_tmins)
# print('sequence length: ', len(unq_tmins))
unq_tmins = np.array(unq_tmins)
# one-hot encoding of categorical static variables
extended_static = [static[0],0,0,static[2],0,0,0,0,static[4]]
if static[1]==0:
extended_static[1] = 1
elif static[1]==1:
extended_static[2] = 1
if static[3]==1:
extended_static[4] = 1
elif static[3]==2:
extended_static[5] = 1
elif static[3]==3:
extended_static[6] = 1
elif static[3]==4:
extended_static[7] = 1
# construct array of maximal size
Parr = np.zeros((max_len,F))
Tarr = np.zeros((max_len,1))
# for each time measurement find index and store
for sample in ts:
tmins = sample[2]
param = sample[-2]
value = sample[-1]
if tmins < max_tmins:
time_id = np.where(tmins==unq_tmins)[0][0]
param_id = np.where(ts_params==param)[0][0]
Parr[time_id, param_id] = value
Tarr[time_id, 0] = unq_tmins[time_id]
length = len(unq_tmins)
# construct dictionary
my_dict = {'id':ID, 'static':static, 'extended_static':extended_static, 'arr':Parr, 'time':Tarr, 'length':length}
# add array into list
PTdict_list.append(my_dict)
print(len(PTdict_list))
np.save('../processed_data/PTdict_list.npy', PTdict_list)
print('PTdict_list.npy saved', PTdict_list[0].keys())
exit(-1)
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