Upload P12

P12data/ReadMe.md

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+# PhysioNet 2012 Challenge dataset #
+
+### Raw data###
+* /rawdata/
+  * Raw data: set-a, set-b, set-c, Outcomes-a.txt, Outcomes-b.txt, Outcomes-c.txt
+  * Data source: https://www.physionet.org/content/challenge-2012/1.0.0/
+
+### Data parsing (process_scripts) ###
+Run following scripts in turn:
+* /process_scripts/
+  * ParseData.py : generate arr\_outcomes.npy, ts\_params.npy, static\_params.npy, and P\_list.npy
+  * IrregularSampling.py: generate: extended\_static\_params.npy, PTdict\_list.npy
+  * Generate\_splitID.py: generate phy12\_splitX.npy where X range from 1 to 5. Only contains the IDs. Train/val/test =  9:1:1
+
+Note: PTdict\_list.npy and arr\_outcomes.npy are the most important files.
+
+
+### Processed data ###:
+* /processed_data/
+  * PTdict_list.npy
+  * arr_outcomes.npy  
+  * ts_params.npy
+  * extended_static_params.npy
+  
+  * static_params.npy
+  * P_list.npy
+* /splits/
+  * phy12\_splitsX.npy  where X range from 1 to 5. In splits folder: there are 5 npy files, each contains three array representing idx\_train,idx\_val,and idx\_test.
+  

P12data/process_scripts/Generate_splitID.py

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+import numpy as np
+
+# arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)
+
+# split randomization over folds
+"""Use 9:1:1 split"""
+p_train = 0.80
+p_val   = 0.10
+p_test  = 0.10
+
+
+n =  11988  # original 12000 patients, remove 12 outliers
+n_train = round(n*p_train)
+n_val   = round(n*p_val)
+n_test  = n - (n_train+n_val)
+print(n_train, n_val, n_test)
+Nsplits = 5
+for j in range(Nsplits):
+    p = np.random.permutation(n)
+    idx_train = p[:n_train]
+    idx_val   = p[n_train:n_train+n_val]
+    idx_test  = p[n_train+n_val:]
+    np.save('../splits/phy12_split'+str(j+1)+'.npy', (idx_train, idx_val, idx_test))
+
+    # np.save('../splits/phy12_split_subset'+str(j+1)+'.npy', (idx_train, idx_val, idx_test))
+print('split IDs saved')
+
+# # check first split
+# idx_train,idx_val,idx_test = np.load('../splits/phy12_split1.npy', allow_pickle=True)
+# print(len(idx_train), len(idx_val), len(idx_test))

P12data/process_scripts/IrregularSampling.py

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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)
+
+
+

P12data/process_scripts/ParseData.py

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+# Parsing of EHR records from PhysioNet-2012 database
+# 
+# Author: Theo Tsiligkaridis
+# Last updated: April 1 2021
+from IPython.core.display import display, HTML
+display(HTML("<style>.container { width:85% !important; }</style>"))
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+df_outcomes_a = pd.read_csv('../rawdata/Outcomes-a.txt', sep=",", header=0,
+                            names=["RecordID","SAPS-I","SOFA","Length_of_stay","Survival","In-hospital_death"])
+df_outcomes_b = pd.read_csv('../rawdata/Outcomes-b.txt', sep=",", header=0,
+                            names=["RecordID","SAPS-I","SOFA","Length_of_stay","Survival","In-hospital_death"])
+df_outcomes_c = pd.read_csv('../rawdata/Outcomes-c.txt', sep=",", header=0,
+                            names=["RecordID","SAPS-I","SOFA","Length_of_stay","Survival","In-hospital_death"])
+print(df_outcomes_a.head(n=5))
+print(df_outcomes_b.head(n=5))
+print(df_outcomes_c.head(n=5))
+
+arr_outcomes_a = np.array(df_outcomes_a)
+arr_outcomes_b = np.array(df_outcomes_b)
+arr_outcomes_c = np.array(df_outcomes_c)
+
+n_a = arr_outcomes_a.shape[0]
+n_b = arr_outcomes_b.shape[0]
+n_c = arr_outcomes_c.shape[0]
+print('n_a = %d, n_b = %d, n_c = %d' % (n_a,n_b,n_c))
+
+# merge dataframes
+arr_outcomes = np.concatenate([arr_outcomes_a, arr_outcomes_b, arr_outcomes_c], axis=0)
+n = arr_outcomes.shape[0]
+print(arr_outcomes.shape)
+
+y_inhospdeath = arr_outcomes[:,-1]
+print("Percentage of in-hosp death: %.2f%%" % (np.sum(y_inhospdeath)/n*100))
+print(y_inhospdeath.shape)
+
+
+# Store outcomes in npy format
+np.save('../processed_data/arr_outcomes.npy', arr_outcomes)
+print('arr_outcomes.npy saved')
+
+# arr_outcomes = np.load('phy12_outcomes.npy')
+# print(arr_outcomes.shape)
+
+
+# Map parameter strings into columns
+import os
+
+# extract all parameters encountered across all patients
+def extract_unq_params(path):
+    cnt = 0
+    for f in os.listdir(path):
+        file_name, file_ext = os.path.splitext(f)
+        if file_ext == '.txt':
+            df_temp = pd.read_csv(path+file_name+'.txt', sep=",", header=1, names=["time", "param", "value"])
+            arr_data_temp = np.array(df_temp)
+    #         print(arr_data_temp[:10])
+            params_temp = arr_data_temp[:,1] # extract variable names
+            if cnt==0:
+                params_all = params_temp
+            else:
+                params_all = np.concatenate([params_all, params_temp], axis=0)
+            cnt += 1
+#             print(cnt)
+#     print("Processed %d patient records in path: %s" % (cnt,path))
+    params_all = list(params_all)
+    # filter out nan
+    params_all = [p for p in params_all if str(p) != 'nan']
+    # create list of parameters
+    param_list = list(np.unique(np.array(params_all)))
+    return param_list
+
+param_list_a = extract_unq_params('../rawdata/set-a/')
+param_list_b = extract_unq_params('../rawdata/set-b/')
+param_list_c = extract_unq_params('../rawdata/set-c/')
+
+param_list = param_list_a + param_list_b + param_list_c
+param_list = list(np.unique(param_list))
+
+# remove 5 fields
+param_list.remove("Gender")
+param_list.remove("Height")
+param_list.remove("Weight")
+param_list.remove("Age")
+param_list.remove("ICUType")
+
+print("Parameters: ", param_list)
+print("Number of total parameters:", len(param_list))
+
+# save variable names
+np.save('../processed_data/ts_params.npy', param_list)
+print('ts_params.npy: the names of 36 variables')
+# del(param_list_a, param_list_b, param_list_c, param_list)
+
+
+# # form data structure for a single patient
+#
+# # load data for a single patient and process
+# df = pd.read_csv('../rawdata/set-a/132612.txt', sep=",", header=1, names=["time", "param", "value"])
+# df_demogr = df.iloc[0:5]
+# df_data   = df.iloc[5:]
+# # print(df_demogr.head(n=10))
+# # print(df_data.head(n=10))
+#
+# # convert to array format
+# arr_demogr = np.array(df_demogr)
+# arr_data = np.array(df_data)
+# # print(arr_demogr)
+# print(arr_data[:10])
+#
+# # group into a dictionary if param is in params_list (36)
+# # my_dict['id'] = '132612'
+# my_dict = {'id': '132612'}
+# # demographics
+# my_dict['static'] = (arr_demogr[0,2], arr_demogr[1,2], arr_demogr[2,2], arr_demogr[3,2], arr_demogr[4,2])
+# # time-series
+# n_pts = arr_data.shape[0]
+# print(n_pts)
+# ts_list = []
+# for i in range(n_pts):
+#     param = arr_data[i,1]
+#     if param in params_list:
+#         ts = arr_data[i,0]
+#         hrs, mins = float(ts[0:2]), float(ts[3:5])
+#         value = arr_data[i,2]
+#         totalmins = 60.0*hrs + mins
+#         ts_list.append((hrs,mins,totalmins,param,value))
+# my_dict['ts'] = ts_list
+# print(my_dict['static'])
+# print(my_dict['ts'])
+#
+#
+# # In[18]:
+
+
+static_param_list = ['Age','Gender','Height','ICUType','Weight']
+np.save('../processed_data/static_params.npy', static_param_list)
+print('save names of static descriptors: static_params.npy')
+
+
+# form data structures for all patients and store on disk
+
+def parse_all(path):
+    P_list = []
+    cnt = 0
+    allfiles = os.listdir(path)
+    allfiles.sort()
+    for f in allfiles:
+    #for f in os.listdir(path):
+        file_name, file_ext = os.path.splitext(f)
+        if file_ext == '.txt':
+            df = pd.read_csv(path+file_name+'.txt', sep=",", header=1, names=["time", "param", "value"])
+            df_demogr = df.iloc[0:5]
+            df_data   = df.iloc[5:]
+            
+            arr_demogr = np.array(df_demogr)
+            arr_data = np.array(df_data)
+            
+#             print(file_name)
+            
+            # construct dictionary
+            my_dict = {'id': file_name}
+
+            # demographics
+            my_dict['static'] = (arr_demogr[0,2], arr_demogr[1,2], arr_demogr[2,2], arr_demogr[3,2], arr_demogr[4,2])
+
+            # time-series
+            n_pts = arr_data.shape[0]
+            ts_list = []
+            for i in range(n_pts): # for each line
+                param = arr_data[i,1] # the name of variables
+                if param in param_list:
+                    ts = arr_data[i,0]  # time stamp
+                    hrs, mins = float(ts[0:2]), float(ts[3:5])
+                    value = arr_data[i,2]  # value of variable
+                    totalmins = 60.0*hrs + mins
+                    ts_list.append((hrs,mins,totalmins,param,value))
+            my_dict['ts'] = ts_list
+            
+            # append patient dictionary in master dictionary
+            P_list.append(my_dict)
+            cnt += 1
+    return P_list
+
+# Merge lists of patients into master list
+p_list_a = parse_all('../rawdata/set-a/')
+p_list_b = parse_all('../rawdata/set-b/')
+p_list_c = parse_all('../rawdata/set-c/')
+P_list = p_list_a + p_list_b + p_list_c
+print('Length of P_list', len(P_list))
+
+np.save('../processed_data/P_list.npy', P_list)
+print('P_list.npy saved')
+
+# # Store master list and labels
+# import json
+#
+# with open("phy12_data.json", 'w') as f:
+#     # indent=2 is not needed but makes the file human-readable
+#     json.dump(P_list, f, indent=2)
+#
+# with open("phy12_data.json", 'r') as f:
+#     P_list = json.load(f)
+#
+# print(len(P_list))
+
+
+
+

P12data/process_scripts/Splits_phy12.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:90% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train/test/val splits for PhysioNet-2012 dataset\n",
+    "# \n",
+    "# Author: Theodoros Tsiligkaridis\n",
+    "# Last updated: April 5 2021\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
+    "\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# p_train = 0.80*0.80\n",
+    "# p_val   = 0.80*0.20\n",
+    "# p_test  = 0.20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12000\n",
+      "(12000, 6)\n",
+      "pid:  132539\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"phy12_data.json\", 'r') as f:\n",
+    "    P_list = json.load(f)\n",
+    "print(len(P_list))\n",
+    "\n",
+    "arr_outcomes = np.load('phy12_outcomes.npy')\n",
+    "print(arr_outcomes.shape)\n",
+    "\n",
+    "pid = P_list[0]['id']\n",
+    "print('pid: ', pid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11988 (11988, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# remove blacklist patients\n",
+    "blacklist = ['140501', '150649', '140936', '143656', '141264', '145611', '142998', '147514', '142731', '150309', '155655', '156254']\n",
+    "\n",
+    "i = 0\n",
+    "n = len(P_list)\n",
+    "while i<n:\n",
+    "    pid = P_list[i]['id']\n",
+    "    if pid in blacklist:\n",
+    "        P_list = np.delete(P_list, i)\n",
+    "        arr_outcomes = np.delete(arr_outcomes, i, axis=0)\n",
+    "        n -= 1\n",
+    "    i += 1\n",
+    "print(len(P_list), arr_outcomes.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save P_list, arr_outcomes\n",
+    "# with open(\"./data/phy12_data.json\", 'w') as f:\n",
+    "#     # indent=2 is not needed but makes the file human-readable\n",
+    "#     json.dump(P_list, f, indent=2) \n",
+    "np.save('./data/P_list.npy', P_list)\n",
+    "np.save('./data/arr_outcomes.npy', arr_outcomes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '132539', 'static': [54.0, 0.0, -1.0, 4.0, -1.0], 'ts': [[0.0, 7.0, 7.0, 'GCS', 15.0], [0.0, 7.0, 7.0, 'HR', 73.0], [0.0, 7.0, 7.0, 'NIDiasABP', 65.0], [0.0, 7.0, 7.0, 'NIMAP', 92.33], [0.0, 7.0, 7.0, 'NISysABP', 147.0], [0.0, 7.0, 7.0, 'RespRate', 19.0], [0.0, 7.0, 7.0, 'Temp', 35.1], [0.0, 7.0, 7.0, 'Urine', 900.0], [0.0, 37.0, 37.0, 'HR', 77.0], [0.0, 37.0, 37.0, 'NIDiasABP', 58.0], [0.0, 37.0, 37.0, 'NIMAP', 91.0], [0.0, 37.0, 37.0, 'NISysABP', 157.0], [0.0, 37.0, 37.0, 'RespRate', 19.0], [0.0, 37.0, 37.0, 'Temp', 35.6], [0.0, 37.0, 37.0, 'Urine', 60.0], [1.0, 37.0, 97.0, 'HR', 60.0], [1.0, 37.0, 97.0, 'NIDiasABP', 62.0], [1.0, 37.0, 97.0, 'NIMAP', 87.0], [1.0, 37.0, 97.0, 'NISysABP', 137.0], [1.0, 37.0, 97.0, 'RespRate', 18.0], [1.0, 37.0, 97.0, 'Urine', 30.0], [2.0, 37.0, 157.0, 'HR', 62.0], [2.0, 37.0, 157.0, 'NIDiasABP', 52.0], [2.0, 37.0, 157.0, 'NIMAP', 75.67], [2.0, 37.0, 157.0, 'NISysABP', 123.0], [2.0, 37.0, 157.0, 'RespRate', 19.0], [2.0, 37.0, 157.0, 'Urine', 170.0], [3.0, 8.0, 188.0, 'HCT', 33.7], [3.0, 37.0, 217.0, 'GCS', 15.0], [3.0, 37.0, 217.0, 'HR', 80.0], [3.0, 37.0, 217.0, 'NIDiasABP', 52.0], [3.0, 37.0, 217.0, 'NIMAP', 72.67], [3.0, 37.0, 217.0, 'NISysABP', 114.0], [3.0, 37.0, 217.0, 'RespRate', 20.0], [3.0, 37.0, 217.0, 'Temp', 37.8], [3.0, 37.0, 217.0, 'Urine', 60.0], [4.0, 37.0, 277.0, 'HR', 74.0], [4.0, 37.0, 277.0, 'RespRate', 20.0], [5.0, 37.0, 337.0, 'HR', 73.0], [5.0, 37.0, 337.0, 'NIDiasABP', 45.0], [5.0, 37.0, 337.0, 'NIMAP', 66.67], [5.0, 37.0, 337.0, 'NISysABP', 110.0], [5.0, 37.0, 337.0, 'RespRate', 17.0], [5.0, 37.0, 337.0, 'Urine', 170.0], [7.0, 37.0, 457.0, 'GCS', 15.0], [7.0, 37.0, 457.0, 'HR', 64.0], [7.0, 37.0, 457.0, 'NIDiasABP', 49.0], [7.0, 37.0, 457.0, 'NIMAP', 68.33], [7.0, 37.0, 457.0, 'NISysABP', 107.0], [7.0, 37.0, 457.0, 'RespRate', 15.0], [7.0, 37.0, 457.0, 'Temp', 38.1], [7.0, 37.0, 457.0, 'Urine', 120.0], [8.0, 37.0, 517.0, 'HR', 64.0], [8.0, 37.0, 517.0, 'NIDiasABP', 56.0], [8.0, 37.0, 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36.9], [15.0, 37.0, 937.0, 'Urine', 120.0], [16.0, 37.0, 997.0, 'HR', 61.0], [16.0, 37.0, 997.0, 'NIDiasABP', 40.0], [16.0, 37.0, 997.0, 'NIMAP', 58.67], [16.0, 37.0, 997.0, 'NISysABP', 96.0], [16.0, 37.0, 997.0, 'RespRate', 14.0], [17.0, 37.0, 1057.0, 'HR', 65.0], [17.0, 37.0, 1057.0, 'NIDiasABP', 43.0], [17.0, 37.0, 1057.0, 'NIMAP', 61.33], [17.0, 37.0, 1057.0, 'NISysABP', 98.0], [17.0, 37.0, 1057.0, 'RespRate', 13.0], [17.0, 37.0, 1057.0, 'Urine', 140.0], [18.0, 37.0, 1117.0, 'HR', 77.0], [18.0, 37.0, 1117.0, 'RespRate', 18.0], [19.0, 37.0, 1177.0, 'GCS', 15.0], [19.0, 37.0, 1177.0, 'HR', 60.0], [19.0, 37.0, 1177.0, 'NIDiasABP', 55.0], [19.0, 37.0, 1177.0, 'NIMAP', 74.67], [19.0, 37.0, 1177.0, 'NISysABP', 114.0], [19.0, 37.0, 1177.0, 'RespRate', 13.0], [19.0, 37.0, 1177.0, 'Temp', 37.3], [19.0, 37.0, 1177.0, 'Urine', 80.0], [20.0, 37.0, 1237.0, 'HR', 62.0], [20.0, 37.0, 1237.0, 'RespRate', 12.0], [21.0, 37.0, 1297.0, 'HR', 73.0], [21.0, 37.0, 1297.0, 'NIDiasABP', 44.0], [21.0, 37.0, 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1657.0, 'GCS', 15.0], [27.0, 37.0, 1657.0, 'HR', 80.0], [27.0, 37.0, 1657.0, 'NIDiasABP', 39.0], [27.0, 37.0, 1657.0, 'NIMAP', 60.67], [27.0, 37.0, 1657.0, 'NISysABP', 104.0], [27.0, 37.0, 1657.0, 'RespRate', 18.0], [27.0, 37.0, 1657.0, 'Temp', 37.9], [27.0, 37.0, 1657.0, 'Urine', 400.0], [27.0, 37.0, 1657.0, 'Urine', 0.0], [28.0, 37.0, 1717.0, 'Urine', 80.0], [29.0, 37.0, 1777.0, 'HR', 80.0], [29.0, 37.0, 1777.0, 'NIDiasABP', 41.0], [29.0, 37.0, 1777.0, 'NIMAP', 63.33], [29.0, 37.0, 1777.0, 'NISysABP', 108.0], [29.0, 37.0, 1777.0, 'RespRate', 16.0], [29.0, 37.0, 1777.0, 'Urine', 100.0], [30.0, 37.0, 1837.0, 'Urine', 450.0], [31.0, 37.0, 1897.0, 'GCS', 14.0], [31.0, 37.0, 1897.0, 'HR', 78.0], [31.0, 37.0, 1897.0, 'NIDiasABP', 43.0], [31.0, 37.0, 1897.0, 'NIMAP', 65.0], [31.0, 37.0, 1897.0, 'NISysABP', 109.0], [31.0, 37.0, 1897.0, 'RespRate', 18.0], [31.0, 37.0, 1897.0, 'Temp', 38.1], [31.0, 37.0, 1897.0, 'Urine', 220.0], [32.0, 37.0, 1957.0, 'Urine', 210.0], [33.0, 7.0, 1987.0, 'BUN', 8.0], [33.0, 7.0, 1987.0, 'Creatinine', 0.7], [33.0, 7.0, 1987.0, 'Glucose', 115.0], [33.0, 7.0, 1987.0, 'HCO3', 28.0], [33.0, 7.0, 1987.0, 'HCT', 30.3], [33.0, 7.0, 1987.0, 'Mg', 1.9], [33.0, 7.0, 1987.0, 'Platelets', 185.0], [33.0, 7.0, 1987.0, 'K', 4.0], [33.0, 7.0, 1987.0, 'Na', 136.0], [33.0, 7.0, 1987.0, 'WBC', 9.4], [33.0, 37.0, 2017.0, 'HR', 85.0], [33.0, 37.0, 2017.0, 'NIDiasABP', 46.0], [33.0, 37.0, 2017.0, 'NIMAP', 68.33], [33.0, 37.0, 2017.0, 'NISysABP', 113.0], [33.0, 37.0, 2017.0, 'RespRate', 18.0], [33.0, 37.0, 2017.0, 'Urine', 240.0], [34.0, 37.0, 2077.0, 'Urine', 100.0], [35.0, 37.0, 2137.0, 'GCS', 15.0], [35.0, 37.0, 2137.0, 'HR', 83.0], [35.0, 37.0, 2137.0, 'NIDiasABP', 49.0], [35.0, 37.0, 2137.0, 'NIMAP', 75.67], [35.0, 37.0, 2137.0, 'NISysABP', 129.0], [35.0, 37.0, 2137.0, 'RespRate', 20.0], [35.0, 37.0, 2137.0, 'Temp', 38.2], [35.0, 37.0, 2137.0, 'Urine', 100.0], [36.0, 37.0, 2197.0, 'Urine', 50.0], [37.0, 37.0, 2257.0, 'HR', 84.0], [37.0, 37.0, 2257.0, 'NIDiasABP', 47.0], [37.0, 37.0, 2257.0, 'NIMAP', 70.33], [37.0, 37.0, 2257.0, 'NISysABP', 117.0], [37.0, 37.0, 2257.0, 'RespRate', 24.0], [37.0, 37.0, 2257.0, 'Urine', 120.0], [38.0, 37.0, 2317.0, 'RespRate', 21.0], [39.0, 37.0, 2377.0, 'GCS', 15.0], [39.0, 37.0, 2377.0, 'HR', 73.0], [39.0, 37.0, 2377.0, 'NIDiasABP', 57.0], [39.0, 37.0, 2377.0, 'NIMAP', 78.67], [39.0, 37.0, 2377.0, 'NISysABP', 122.0], [39.0, 37.0, 2377.0, 'RespRate', 16.0], [39.0, 37.0, 2377.0, 'Temp', 37.9], [39.0, 37.0, 2377.0, 'Urine', 450.0], [40.0, 37.0, 2437.0, 'HR', 77.0], [40.0, 37.0, 2437.0, 'NIDiasABP', 53.0], [40.0, 37.0, 2437.0, 'NIMAP', 72.0], [40.0, 37.0, 2437.0, 'NISysABP', 110.0], [40.0, 37.0, 2437.0, 'RespRate', 18.0], [41.0, 37.0, 2497.0, 'HR', 73.0], [41.0, 37.0, 2497.0, 'NIDiasABP', 50.0], [41.0, 37.0, 2497.0, 'NIMAP', 69.67], [41.0, 37.0, 2497.0, 'NISysABP', 109.0], [41.0, 37.0, 2497.0, 'RespRate', 14.0], [41.0, 37.0, 2497.0, 'Urine', 450.0], [42.0, 37.0, 2557.0, 'NIDiasABP', 67.0], [42.0, 37.0, 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2857.0, 'Urine', 280.0]]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "P_list = np.load('./data/P_list.npy', allow_pickle=True)\n",
+    "arr_outcomes = np.load('./data/arr_outcomes.npy', allow_pickle=True)\n",
+    "print(P_list[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9590 1199 1199\n"
+     ]
+    }
+   ],
+   "source": [
+    "p_train = 0.80\n",
+    "p_val   = 0.10\n",
+    "p_test  = 0.10\n",
+    "\n",
+    "n = len(P_list)\n",
+    "n_train = round(n*p_train)\n",
+    "n_val   = round(n*p_val)\n",
+    "n_test  = n - (n_train+n_val)\n",
+    "print(n_train, n_val, n_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split randomization over folds\n",
+    "Nsplits = 5\n",
+    "\n",
+    "for j in range(Nsplits):\n",
+    "    p = np.random.permutation(n)\n",
+    "    idx_train = p[:n_train]\n",
+    "    idx_val   = p[n_train:n_train+n_val]\n",
+    "    idx_test  = p[n_train+n_val:]\n",
+    "    np.save('./splits/phy12_new_split'+str(j+1)+'.npy', (idx_train, idx_val, idx_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9590 1199 1199\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check first split\n",
+    "idx_train,idx_val,idx_test = np.load('./splits/phy12_new_split1.npy', allow_pickle=True)\n",
+    "print(len(idx_train), len(idx_val), len(idx_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

P12data/process_scripts/readme

@@ -0,0 +1,9 @@
+Run the following codes in sequence:
+1. ParseData.py: parsing data from rawdataset
+
+2. IrregularSampling.py:permutation, reorgnize the structure of parsed data
+
+3. removeoutliers.py: remove 12 outlier patients based on paper: Set Function fo
+r time series
+
+4. Generate_splitID.py: generate train/val/test IDs by 8:1:1 proportion.

P12data/process_scripts/remove_outliers.py

@@ -0,0 +1,23 @@
+import numpy as np
+
+"""Remove 12 patients at blacklist"""
+PTdict_list = np.load('../processed_data/PTdict_list.npy', allow_pickle=True)
+arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)
+
+
+# remove blacklist patients
+blacklist = ['140501', '150649', '140936', '143656', '141264', '145611', '142998', '147514', '142731', '150309', '155655', '156254']
+
+i = 0
+n = len(PTdict_list)
+while i<n:
+    pid = PTdict_list[i]['id']
+    if pid in blacklist:
+        PTdict_list = np.delete(PTdict_list, i)
+        arr_outcomes = np.delete(arr_outcomes, i, axis=0)
+        n -= 1
+    i += 1
+print(len(PTdict_list), arr_outcomes.shape)
+
+np.save('../processed_data/PTdict_list.npy', PTdict_list)
+np.save('../processed_data/arr_outcomes.npy', arr_outcomes)

P12data/process_scripts/sanity_check.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+P_list = np.load('../processed_data/P_list.npy', allow_pickle=True)
+
+arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)
+
+PTdict_list = np.load('../processed_data/PTdict_list.npy', allow_pickle=True)
+
+print(P_list.shape, P_list[0])
+print(arr_outcomes.shape, arr_outcomes[0])
+print(PTdict_list.shape, PTdict_list[0])

P12data/processed_data/P12_dataset.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c53bb570f70c485651dc469fba4ac201712e46519051e35e9add893a585d325
+size 21765211

P12data/processed_data/PTdict_list.npy

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+version https://git-lfs.github.com/spec/v1
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+size 765972944

P12data/processed_data/P_list.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:131048d34b717b82eb96b1e0120ce7fbdcab8fb2b332e8803348ed73d98cbedc
+size 236056429

P12data/processed_data/arr_outcomes.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eb9b35208d3b0189b301b0f93ddbab60241a7e72a6a75825388968297393d886
+size 575552

P12data/processed_data/extended_static_params.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68cd605e2a6fb50cf42e8e659547c6413ae7aef72fc9e208316d122fb0857b70
+size 452

P12data/processed_data/static_params.npy

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+version https://git-lfs.github.com/spec/v1
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+size 268

P12data/processed_data/ts_params.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7af81fdc463d780eeefe7947caf08340d3403a57dfcb9a05bf45581937b023a4
+size 1712

P12data/rawdata/Outcomes-a.txt

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P12data/rawdata/Outcomes-b.txt

@@ -0,0 +1,4001 @@
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P12data/rawdata/Outcomes-c.txt

@@ -0,0 +1,4001 @@
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