LabelDistributions.ipynb 116 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yaml\n",
    "\n",
    "data_path = yaml.full_load(open('../config.yaml'))['data_path']\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = [8, 8]\n",
    "matplotlib.rcParams['font.size'] = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_labels(df, task):\n",
    "    df['{}_ONSET_HOUR'.format(task)].plot.hist(bins=np.arange(-5, 75, 0.5), alpha=0.9)\n",
    "    plt.xlim(-4,100)\n",
    "    plt.xlabel('{} onset hour'.format(task))\n",
    "    plt.savefig('Onset_{}-histogram.png'.format(task), dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ARF   = pd.read_csv(data_path + 'labels/ARF.csv',   index_col='ICUSTAY_ID')\n",
    "df_Shock = pd.read_csv(data_path + 'labels/Shock.csv', index_col='ICUSTAY_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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daN/PgQuAQwaOOwS4aChUnEMXQLx7RpKkxrbmQWyXV9WRVbVbVW3Xvz67qtZOsF3vBn4nyQuSLE/yEOD1wKeq6oq+zr7ABuDqoWOv7PcxUG+jtlXV94H1Q/UkSVIDE5vQOUlV9XHgKOBMuhGOq4DtgWcMVFsB3FRVG4YOnwKWJdlpoN4N3NkUIya7Jjk6yZoka9atWzer7yFJksafs0GSpwNHAPcH7jK8v6oeN9tGJTkQOAN4O91TZe8NnAB8KMkTRwSMiamqM+lCDitXrqxWnyNJ0rZi3AexHU93F8flwBXArS0aBbwV+GhVvWrgs79GdznkcOB8upGJXZJsPxQ+VgDrq2q6bVPAriM+Y0W/T5IkNTTuyMbRwJsHQ0Aj+9Ld+nq7qroqyc3Ar/ZFa+kurexDd5ll8NjBORprGZqbkWRPYNlQPUmS1MC4czbuDnyyRUOGfA/49cGCJA+ju4Pkmr7oc8CNwJEDdZbRrbdx4cChFwJPSXL3gbJnAzcDn550wyVJ0sbGHdk4F3gycEmDtgw6Azg1yXXcMWfjL+iCxicAquqWJCcDxyeZohulOIYuQJ02dK6XAecnOQXYm27+x9tcY0OSpPbGDRv/ALwlyT2Bixlxl0dVTWLk46/p5oP8Ed2iXDfQLSb26n4tjWkn04WLV9Mtl74GeFJV/WigPVNJDgJOp1uD4wbgVLrAIUmSGhs3bHywf/3v/Tas6OZRzEpVFfC3/baleqv6bXP1rgCeMNt2SZKk8Y0bNh7cpBVL0P6rVs93EyRJWhDGChtV9Z1WDZEkSUvT2CuIJtkxyYuT/F2STyTZpy9/ZpKHTr6JkiRpMRt3Ua996G593Q34Ct1TXpf3uw+ku+30hZNsoCRJWtzGHdn4a+DfgL2AJ3LHE1ihW7NiUo+YlyRJS8S4E0QPAJ5VVT9LMnzXyb8B951MsyRJ0lIx7sjGL4CdN7FvD0Y/XVWSJG3Dxg0bFwOvHlr6u5LsCLyUbtEvjWn/Vau9VVaStGSNexnllXTPJPk2cBHdIl6vAR4O3A141kRbJ0mSFr2xRjaq6vvAo4B30z1J9Xt0k0U/CvxGVV036QZKkqTFbdyRDarqp3TPIpEkSdqisRf1kiRJGse4i3pdTzdPY5Oqao9ZtUiSJC0p415GeRd3DhsrgIOAZcB7JtEoSZK0dIz7ILbXjipPsh1wHrB+Eo2SJElLx0TmbFTVL4F3Ai+bxPkkSdLSMckJog8Edprg+SRJ0hIw7gTRo0cU7wQ8DHgBcP4kGiVJkpaOcSeInjGi7D+BH9JdRvmLWbdIkiQtKeOGjR2HC6pqw4TaIkmSlqBx70YxWEiSpLGMO2fjeePUr6qzx2uOJElaasa9jPI+7ljUKwPlmyozbEiStI0b99bXx9A96fVE4L8A9+lfT+rLH0O3ougK4J6Ta6YkSVqsxh3ZOAX426p680DZj4FvJlkPvKmqDpxY6yRJ0qI37sjGbwFf38S+b9CNbEiSJN1u3LBxLXDUJvYdRbfehiRJ0u3GvYzyWuDsJPsBH6W7hPIrwNOARwLPnWzzJEnSYjfuOhvnJrkGOBb4A+DewI+Afwb+sKq+OPEWSpKkRW3ckQ2q6kvAEQ3aIkmSlqCteuprkl2TPDbJs5Lcoy+701LmkiRJY4WNJNsleQPdRNDPAu8H9u53fzTJX064fZIkaZEbd2RjFfDHwMuBh7DxiqEfppsoKkmSdLtx52y8EDi2qt6ZZPuhfd8BfnUyzZIkSUvFuCMbK4CrN7FvR2A4gEiSpG3cuGHjcuCwTex7CvDV2TVHkiQtNeNeRnkDcG6SnYHz6J7s+ogkhwF/BDx9wu2TJEmL3FgjG1V1PvAC4L8CF9NNED0L+EPgD6rqwkk3UJIkLW5bs6jX2UneDzwM2A34GXBFVf1y0o2TJEmL34zDRpK7AF8BXl5VFwFXNGuVJElaMmZ8GaWqbqEbyah2zZEkSUvNuHejvJ9uzoYkSdKMjDtn4zvAM5N8AfgE3RNfB0c6qqreOanGSZKkxW/csPFX/et9gf1H7C/AsCFJkm43btjwya6SJGksW5yzkeSTSR4KUFUbqmoDcABwl+n3g1vrBkuSpMVlJhNEnwjsOv2mfwDbxcBDWzWq/5wdkhyb5Ookv0hybZJTh+okyXFJfpDk5iSXJXn0iHPtl+SSJOuTXJfkpBEPkpMkSQ2MvahXL1uuMmtnAU8ATgTWAnsC+w3VORY4HnhlX+cYYHWSR1TVvwEkWQGsplsX5HC6J9O+lS5ovbb5t5AkaRu3tWGjqSQHA88GHlVVIxcP6xcZOxZ4Y1Wd3pd9HrgGeCl3BImXAHcFjqiqG4GLkywHTkjypr5MkiQ1MtN1NkYt5NVyca8XAZ/aVNDoPQ5YDpx7e4Oqfg5cABwyUO8Q4KKhUHEOXQA5YGItliRJI800bFyU5MdJfgxc35ddMl02uE2oXY8BvpXk9CQ39nMtzk+yx0CdfYENwNVDx17Z7xust3awQlV9H1g/VE+SJDUwk8soJzZvxZ3dBzgK+DrwHODuwJuADyX5raoqYAVw04g7YKaAZUl2qqpb+3o3jPiMqX7fRpIcDRwN8IAHPGAy30aSpG3YFsNGVc1H2Ei/HV5VPwVIcj3wabpJo5e0+uCqOhM4E2DlypU+B0aSpFka99koc2UK+JfpoNH7DHArd9yRMgXsMuIW1hXA+n5UY7rertzZin6fJElqaKGGjSsZfXttgF/2f14LbA/sM1RneI7GWobmZiTZE1g2VE+SJDWwUMPGx4BHJtltoOzxdMulf71//zngRuDI6QpJlgGHARcOHHch8JQkdx8oezZwM91lGUmS1NBCDRtnAj8FLkhyWJLnAX8PrK6qzwBU1S3AycBxSf44yUHAeXTf6bSBc50B/AI4P8kT+wmgJwBvW2hrbOy/ajX7r1o9382QJGmiFuSiXlV1Y5InAH9NtybGrcBHgJcPVT2ZLly8GrgXsAZ4UlX9aOBcU30QOZ1uDY4bgFPpAockSWpsQYYNgKr6NnDoFuoUsKrfNlfvCrq7WCRJ0hxbqJdRJEnSEmHYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJThg1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1JRhQ5IkNWXYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJThg1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1JRhQ5IkNWXYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJTO8x3A3Rn+69affufv/SaJ85jSyRJmj1HNiRJUlOGDUmS1JRhQ5IkNWXYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJThg1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1NSiCBtJ7pfkpiSVZJeB8iQ5LskPktyc5LIkjx5x/H5JLkmyPsl1SU5Ksv3cfgtJkrZNiyJsAG8GbhpRfixwPHAKcFhfZ3WS+0xXSLICWA0UcDhwEvAK4MTGbZYkSSyCsJHk8cDBwFuGyu9CFzbeWFWnV9Vq4Ei6UPHSgaovAe4KHFFVF1fVGXRB45gky+fiO0iStC1b0GGjv9RxGt1oxE+Gdj8OWA6cO11QVT8HLgAOGah3CHBRVd04UHYOXQA5oEGzJUnSgAUdNuhGJXYG3jFi377ABuDqofIr+32D9dYOVqiq7wPrh+pJkqQGFmzYSHIv4HXAMVV124gqK4CbqmrDUPkUsCzJTgP1bhhx/FS/b/hzj06yJsmadevWbf0XkCRJwAIOG8Aq4AtV9Ym5/NCqOrOqVlbVyt13330uP1qSpCVph/luwChJHg68CHh8knv0xcv6112TbKAbmdglyfZDoxsrgPVVdWv/fgrYdcTHrOj3SZKkhhZk2AAeDOwIfH7EvmuBdwFnA9sD+wBXDewfnqOxlqG5GUn2pAsvG83lkCRJk7dQw8ZngAOHyg4GXgUcCnwX+B5wI93trq8HSLKMbr2NMweOuxB4ZZK7V9V/9GXPBm4GPt3qC0iSpM6CDBtV9RPg0sGyJHv1f/ynqrqpLzsZOD7JFN0oxTF081BOGzj0DOBlwPlJTgH2Bk4A3jZ0O6wkSWpgQYaNMZxMFy5eDdwLWAM8qap+NF2hqqaSHAScTrcGxw3AqXSBQ5IkNbZowkZVnQWcNVRWdHetrNrCsVcAT2jVNkmStGkL+dZXSZK0BBg2JElSU4YNSZLUlGFDkiQ1ZdhY4PZftZr9V62e72ZIkrTVDBuSJKkpw4YkSWrKsCFJkpoybEiSpKYMG5IkqSnDhiRJasqwIUmSmjJsSJKkpgwbkiSpKcOGJElqyrAhSZKaMmxIkqSmDBuSJKkpw4YkSWrKsCFJkpoybEiSpKYMG5IkqSnDhiRJasqwIUmSmjJsSJKkpgwbkiSpKcOGJElqyrCxSOy/ajX7r1o9382QJGlshg1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1JRhQ5IkNWXYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJThg1JktSUYUOSJDVl2FhkfNS8JGmxMWxIkqSmDBuSJKkpw4YkSWpqQYaNJEcm+WiSHya5KcmXkzx3RL0XJ7k6yS19nYNG1Llfkg8l+Y8kP0lyepJlc/NNJEnSggwbwDHATcDLgacB/wicneRPpiv04eMM4L3AIcDlwMeSPGKgzo7ARcADgecAfwocCZw5N19DkiTtMN8N2ITDquonA+8/lWQPuhByWl92AvCeqnodQJJPA78GHAv8fl/nmcDDgH2q6l/7ercB5yQ5saqubv5NJEnaxi3IkY2hoDHtq8AeAEn2Bh4CnDtwzC+B8+hGOaYdAvzzdNDofRi4FTh4ws2WJEkjLMiwsQmPBb7V/3nf/nXtUJ0rgXsm2X2g3kZ1qupW4DsD55AkSQ0tirDRT/x8OvDWvmhF/3rDUNWpof0rRtSZrrdiRDlJjk6yJsmadevWbX2jJUkSsAjCRpK9gLOBj1TVWa0/r6rOrKqVVbVy99133/IBkiRpsxZ02EhyT+BC4HvA8wd2TY9g7Dp0yIqh/VMj6kzXmxpRLkmSJmzBho1+LYyPATsBT62q9QO7p+dhDM+72Bf4WVWtG6i3UZ0kOwF7c+f5HpIkqYEFGTaS7EB3Z8mDgYOr6seD+6vqu3STRY8cOGa7/v2FA1UvBH4zyQMHyp4G7Az8Q5vWS5KkQQt1nY2/AQ6lW4TrXknuNbDvq1X1C7p1Nt6X5Brgs8AL6cLJ8wbqfhB4DXB+kuPpLqmcCpztGhuSJM2NhRo2nty/vn3EvgcB11TV+5PsArwKOJ5uBdGnVtU3pytW1W1JDgZOp1uT4xfAOcArWzZekiTdYUGGjaraa4b13gm8cwt1rqW7bVaSJM2DBTlnQ1u2/6rV7L9q9Xw3Q5KkLTJsSJKkpgwbkiSpKcPGIuflFEnSQmfYkCRJTRk2JElSU4YNSZLUlGFDkiQ1ZdiQJElNGTYkSVJThg1JktSUYWOJcL0NSdJCZdiQJElNGTYkSVJThg1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1JRhQ5IkNWXYkCRJTe0w3w3QZA2uIvql1zxxHlsiSVLHkQ1JktSUYUOSJDVl2JAkSU0ZNiRJUlOGDUmS1JRhYwnbf9Xqje5OkSRpPhg2JElSU4YNSZLUlGFjG+DlFEnSfDJsSJKkpgwbkiSpKcOGJElqyrAhSZKaMmxsg5wwKkmaS4YNSZLUlGFDkiQ1tcN8N0Bzx0snkqT5YNiYsMX0gz7c1i+95onz1BJJ0lLmZRRJktSUIxu63ahRmZmOdszm2HFMf46jMJK0eDiyobF426wkaVyObGhGhgPG1owwDB8zzjk29fmDHO2QpIXJsKHNmsQoxkyCwvC+4UAiSVq8DBtakGYTMjY1YrK50RDvzJGkdraJsJFkP+A04LHADcD/Ak6sqg3z2rAlYCajFLM5x2zM5DKNIyeS1N6SDxtJVgCrgSuAw4FfBd5KNzn2tfPYNM2RSY6SzGR0xFGRmbPPpG3Dkg8bwEuAuwJHVNWNwMVJlgMnJHlTX6YlYJKjFOPMM5nbqORFAAAMw0lEQVRpO7bmB3VzP8ZbuvSzNcfO5Htuqe5sgsPgOcf5Pls6VtL8SlXNdxuaSnIZcF1VPWeg7AHA94CnVdUFmzp25cqVtWbNmrE+z2F56c5mGlC2Zl7N5v4/t6XAMpO7miY5+rK5z5/N587mTq9x2riptmppSfLlqlo50XNuA2Hjx8DfVNUJQ+U/B06oqjdv6thRYWOc/7qStDBtzd1OM5lMPJsRo3GP2dznzjfDyOJm2NgKSW4DXllVfzVUfi3w3qo6bqj8aODo/u1Dgatm2YTdgJ/M8hy6M/u1Dfu1Dfu1Dfu1jYdW1d0necJtYc7GWKrqTODMSZ0vyZpJJ0TZr63Yr23Yr23Yr20kGW/+wAxsC8uVTwG7jihf0e+TJEkNbQthYy2w72BBkj2BZf0+SZLU0LYQNi4EnpJk8PrTs4GbgU/PwedP7JKMNmK/tmG/tmG/tmG/tjHxft0WJoiuoFvQ65vAKcDewNuAv6oqF/WSJKmxJR824Pblyk9n4+XKT3C5ckmS2tsmwoYkSZo/28KcjXmRZL8klyRZn+S6JCcl2X6+27VYJDkyyUeT/DDJTUm+nOS5I+q9OMnVSW7p6xw0H+1djJLcr+/bSrLLQHmSHJfkB0luTnJZkkfPZ1sXgyQ7JDm2//v4iyTXJjl1qI59O4Ykz0nylf7v6Q+TvDfJHkN17NPNSLJPkr9L8o0kG5JcOqLOjPpwNr9rho0GBh7+VnQPfzsJeAVw4ny2a5E5BrgJeDnwNOAfgbOT/Ml0hT58nAG8FzgEuBz4WJJHzH1zF6U30/XxsGOB4+nmOB3W11md5D5z2LbF6CzgZcBbgCfT9ePNQ3Xs2xlK8jTg/cDn6P4dfRXweODjSQZ/u+zTzXs4cCjdApXf2kSdLfbhrH/Xqsptwhvwaro1PJYPlP05sH6wzG2zfbjbiLKzgX8deH8V8O6B99sB/wK8b77bv9A3un+0fwb8Wf+Pxy59+V2Afwf+YqDu3YB1wOvnu90LdQMOBm4D9ttMHft2vD49B/jyUNnT+r+vD7NPZ9yP2w38+YPApUP7Z9SHs/1dc2SjjUOAi2rjJ8qeQ/f02QPmp0mLS1WNWoL4q8AeAEn2Bh4CnDtwzC+B8+j6X5vQD3ueRvdfJsP9/DhgORv368+BC7BfN+dFwKeq6orN1LFvx7Mj3Y/goBv61/Sv9ukW9P8ubs5M+3BWv2uGjTb2ZWjBsKr6Pl0C3HfkEZqJx3LHMOB0Pw4vzHYlcM8ku89ZqxaflwA7A+8YsW9fYANw9VD5lfh3d3MeA3wryelJbuyvaZ8/NL/Avh3Pu4HfSfKCJMuTPAR4PRuHOvt09mbah7P6XTNstLGCOxL4oKl+n8bUT/x8OvDWvmi6H4f7eWpovwYkuRfwOuCYqrptRJUVwE1159vCp4BlSXZq3cZF6j7AUcCjgecAfwD8BvChJNP/FW7fjqGqPk7Xp2fSjXBcBWwPPGOgmn06ezPtw1n9rvkgNi14Sfaim6/xkao6a14bs/itAr5QVZ+Y74YsMem3w6vqpwBJrqdbpfgJwCXz2LZFKcmBdBPA3063EvS9gRPoAtwTR/w4agEzbLThw98mJMk96f6h+R7w/IFd0/24Kxun7RVD+9VL8nC6uQWPT3KPvnhZ/7prkg10/bZLku2H/jFfAayvqlvnrsWLyhTw3emg0fsMcCuwH13YsG/H81bgo1X1qumCJF+jG8o/HDgf+3QSZtqHs/pd8zJKGz78bQKSLAM+BuwEPLWq1g/snu7H4WuF+wI/q6p1c9DExebBdJPuPk/3j8MUd8zbuJZu0uhauqHqfYaOvdP1Wm3kSu6YtDgowPQEPft2PPsCXxssqKqr6G4n/tW+yD6dvZn24ax+1wwbbcz3w98WvSQ70N1Z8mDg4Kr68eD+qvou3WTRIweO2a5/f+EcNnUx+Qxw4NB2Sr/vULp1Nz4H3MjG/bqM7t57+3XTPgY8MsluA2WPpwt3X+/f27fj+R7w64MFSR5Gd/fDNX2RfTp7M+3D2f2uzfc9wEtxoxtWuh64GHgicDTdIine9z3zPjyT7n76lwG/NbTt3Nd5Lt0s6tfS/XCe1f/Ff8R8t3+xbHQT8G5fZ6MvezXdDPM/Bg4CPk53i+y957u9C3Wju3Xw+3SjRocBzwN+AFw8VM++nXmf/indqNBb+39Hn083SfRfgbvZpzPux2XAM/vt83SLH06/XzbTPpzt79q8d8RS3eiu036q//G7nu4OgO3nu12LZaP7L5faxLbXQL0XA98GfgF8BThovtu+mLZNhI0Ar6G7tHIz8E/Ar813Wxf6RjcM/Qng53SXqM4CVgzVsW9n3p8B/gj4Rt+nPwQ+AOxtn47Vj3tt6d/SmfbhbH7XfBCbJElqyjkbkiSpKcOGJElqyrAhSZKaMmxIkqSmDBuSJKkpw4YkSWrKsCEtckn+NUklGV5umCS/2++b3v49yReTPH1E3WuG6k5vvz8332T2kjwryVEzrFtJXtq4SZIwbEiLWpLH0i3aA92KqpvyfOCxfZ2fAucnefyIemf39Qa3f5hUe+fAs+gWKpO0gPjUV2lxey7d6orf7P/8uk3U+0ZVfRMgyaV0S2n/PnDZUL3rq+oLbZqqQUm2p1t90SeTaslzZENapPofq2cBHwXeDTwsyaO2dFx1T8/9NrDnhNrxrCT/kuQXSX6QZFX/IL3p/Uf1lywemeTiJD9PsjbJEUPn+e0k/5Tkxn77WpIjh+r8jySX95/1vSR/PrDvLOAZwAEDl4BO2ELzt0/yhiTrkvw4yTuS7Dz0mY9OckmS9UmmkvyfJPce2D99qeoRQ8ddmuSDg+1LsibJ05NcDtwCPGYL7ZOWBMOGtHgdCNwbOAf4IHAbm7+UAtz+dNz70z3QasTu7DCwbb+Fcz2Z7nkVXwEOp3tM/Z8Bp4+ofjZdMPo94GrgnCT378+znO7Jqd+lCwzPBP4euMfAZ70S+Fvgw8BT+z+/bmDexeuAfwS+yh2XgP7XZjsDXgHsQTfK82bgD+keADb9mbsDl9I9zOp5wJ8ABwAXJ9lpC+ceZS/gTcAbgUMY/b+BtPTM90Ni3Nzctm4D3kX3wK+d+vcfo3uAXQbq/C7dA5ceRXfZdHe6H9UbgIcOne8a7vygpmu30IYvAP84VPbndE/jvX///qj+XC8aqHMv4D+Bl/TvV/Z17r6Jz1lO94TJvxwqPwn4N/qHQdGFrktn2H8FXDZU9mHgCwPvT+77avlA2WP6Y5871MePGDrXpcAHB96f1dd79Hz/3XFzm+vNkQ1pEer/q/oI4EN1xzX/c4AH0v0X/bCv0Y18/Bg4Bjiqqq4aUe99wG8ObIdupg3bA78OnDe06wN0o6bD7fjk9B+q6qd9W+7fF32HLkycneTwJPcYOvaxwN2A8wZHXuieQHnvgfOM65ND768YOtf+wCer6saBtn+RLpj99lZ83g+r6mtbcZy0qBk2pMXpELpLDJ9Ico/+x/lS4BeMvpTyHLrw8AzgKuB/J9ljRL0fVdWage0bm2nDbsCOwI+Gz9G/3nOo/Iah97cCdwGoqingSf35zgXWJfl4kr0HPgvgcrrQNL39Y1++tfNPNtmm3n258/ejLxv+fjMx6lzSkmfYkBan6UBxHt2llCm6O0x2Bo4cMdfi8j48nA8cRjcH4fhZtuEndD/4vzJUPj158mfjnKyqvlBVB9OFqCOAh9DN8xg811PZeORlevv6uI2foeu58/eD7jtOt+mW/nV4DseKEcfVhNolLSqGDWmRSXI3usDwfrpJooPbMXQ/hE/Y1PFV9R26iZNHJRn1QzojVbUB+DJw5NCuZwG/BD6/lee9uaouoLvDZr+++PPAzcAeQyMv09t/9PWGRyZm64vAU5LcfbogyW/STfT8TF90bf/6sIE6ewL7TrAd0qLmOhvS4nM43cjE2/v5A7dL8lngNXQjHxdv5hxvAl5Md3fFbEY4/hK4KMn/ppsz8ki6u0LeWVXXbvbIAUn+K/Aiugma3wfuR3dnyKcAquqG/jbWtyd5IN36INvRjX4cWFW/159qLXB4v0LqtcB1VXXdLL7f24A/6r/jKcAudJNG/wX4v33brk2yhu7OmPV9u45jzJEdaSlzZENafJ4LXD0cNACq6ja6OQ9HDK8XMVTve3STQf9nP1KyVarqk3TzQVYCFwD/P/BWYNxlwL9Nd4nhDXSTNt9Et3LpiwY+603A0XTzVT5CN7LzfOCfBs7zN/3x7wb+ua+/1apqHd2I0S39572j/7wn1caLcT2XLiS9r/8OJ9HNjZFEf4ucJElSK45sSJKkpgwbkiSpKcOGJElqyrAhSZKaMmxIkqSmDBuSJKkpw4YkSWrKsCFJkpr6f5wT5Qho37yvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_labels(df_ARF, 'ARF')\n",
    "visualize_labels(df_Shock, 'Shock')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "cutoff_h = 4\n",
    "mimic3_path = yaml.full_load(open('config.yaml'))['mimic3_path']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients = pd.read_csv(mimic3_path + 'PATIENTS.csv', parse_dates=['DOB', 'DOD'], usecols=['SUBJECT_ID', 'DOB', 'DOD'])\n",
    "admissions = pd.read_csv(mimic3_path + 'ADMISSIONS.csv', parse_dates=['DEATHTIME'], usecols=['SUBJECT_ID', 'HADM_ID', 'DEATHTIME'])\n",
    "examples = pd.read_csv(data_path + 'prep/icustays_MV.csv', parse_dates=['INTIME', 'OUTTIME']).sort_values(by='ICUSTAY_ID') # Only Metavision\n",
    "\n",
    "examples = pd.merge(examples, patients, on='SUBJECT_ID', how='left')\n",
    "examples = pd.merge(examples, admissions, on=['SUBJECT_ID', 'HADM_ID'], how='left')\n",
    "examples['AGE'] = examples.apply(lambda x: (x['INTIME'] - x['DOB']).total_seconds(), axis=1) / 3600 / 24 / 365.25\n",
    "\n",
    "examples['LOS'] = examples['LOS'] * 24 # Convert to hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.593499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.971162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.151600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.996050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.835000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.739000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                LOS\n",
       "count  23620.000000\n",
       "mean       3.593499\n",
       "std        4.971162\n",
       "min        0.000400\n",
       "25%        1.151600\n",
       "50%        1.996050\n",
       "75%        3.835000\n",
       "max      101.739000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(examples[['LOS']] / 24.).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove non-adults\n",
    "min_age = 18\n",
    "max_age = np.inf # no max age\n",
    "examples = examples[(examples.AGE >= min_age) & (examples.AGE <= max_age)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23593"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examples['ICUSTAY_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ICUSTAY_ID</th>\n",
       "      <th>DEATHTIME</th>\n",
       "      <th>INTIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200038</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2143-10-24 20:35:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200040</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2153-10-24 16:01:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>200049</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2118-08-28 08:56:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>200050</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2149-07-14 17:51:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>200053</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2166-02-27 18:45:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>200055</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2179-03-17 09:59:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>200061</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2134-01-23 16:38:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>200063</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-03-09 23:20:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>200067</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2126-06-29 18:45:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>200069</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2195-12-12 02:12:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>200072</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2106-03-03 19:39:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>200075</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2159-09-23 00:13:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>200079</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2158-01-02 20:54:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>200081</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2142-03-01 16:46:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>200087</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2196-08-30 11:19:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>200094</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2198-06-21 12:27:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>200095</td>\n",
       "      <td>2113-10-31 00:20:00</td>\n",
       "      <td>2113-10-27 15:23:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>200098</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2136-03-27 11:50:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>200099</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2163-06-21 19:49:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>200104</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2198-02-24 12:33:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>200105</td>\n",
       "      <td>2104-10-23 00:01:00</td>\n",
       "      <td>2104-10-23 18:30:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>200108</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2110-12-10 01:18:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>200116</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2198-03-19 20:16:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>200126</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2126-12-14 11:29:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>200131</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2176-10-30 12:05:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>200133</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2172-02-14 14:53:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>200135</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2145-10-12 18:46:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>200138</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2190-07-28 19:41:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>200141</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2143-10-15 11:24:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>200143</td>\n",
       "      <td>2191-04-28 14:31:00</td>\n",
       "      <td>2191-04-01 21:45:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23590</th>\n",
       "      <td>299867</td>\n",
       "      <td>2186-08-02 02:31:00</td>\n",
       "      <td>2186-07-15 16:55:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23591</th>\n",
       "      <td>299871</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2170-02-02 22:33:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23592</th>\n",
       "      <td>299872</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2192-02-22 14:52:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23593</th>\n",
       "      <td>299875</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2155-06-05 15:59:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23594</th>\n",
       "      <td>299879</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2174-08-15 09:23:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23595</th>\n",
       "      <td>299880</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2159-06-02 20:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23596</th>\n",
       "      <td>299883</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2165-05-24 17:07:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23597</th>\n",
       "      <td>299889</td>\n",
       "      <td>2172-06-09 04:30:00</td>\n",
       "      <td>2172-05-30 09:05:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23598</th>\n",
       "      <td>299898</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2169-09-16 04:29:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23599</th>\n",
       "      <td>299901</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2186-11-26 17:23:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23600</th>\n",
       "      <td>299904</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2187-01-21 00:25:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23601</th>\n",
       "      <td>299909</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2182-04-25 04:08:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23602</th>\n",
       "      <td>299913</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2121-07-21 02:08:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23603</th>\n",
       "      <td>299914</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-08-16 23:08:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23604</th>\n",
       "      <td>299921</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2103-05-08 09:43:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23605</th>\n",
       "      <td>299923</td>\n",
       "      <td>2158-12-13 17:36:00</td>\n",
       "      <td>2158-12-13 13:14:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23606</th>\n",
       "      <td>299928</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2137-11-06 17:23:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23607</th>\n",
       "      <td>299929</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2145-01-14 22:33:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23608</th>\n",
       "      <td>299930</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2194-07-04 12:50:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23609</th>\n",
       "      <td>299933</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2105-08-03 14:40:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23610</th>\n",
       "      <td>299943</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2151-08-25 03:29:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23611</th>\n",
       "      <td>299947</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2116-11-27 21:10:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23612</th>\n",
       "      <td>299948</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2119-05-25 13:47:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23613</th>\n",
       "      <td>299949</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2118-07-16 05:42:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23614</th>\n",
       "      <td>299950</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2122-06-20 13:25:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23615</th>\n",
       "      <td>299956</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2177-05-29 07:38:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23616</th>\n",
       "      <td>299957</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2132-10-13 10:41:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23617</th>\n",
       "      <td>299962</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2195-11-25 19:04:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23618</th>\n",
       "      <td>299979</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2127-12-05 02:16:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23619</th>\n",
       "      <td>299998</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2181-07-05 18:47:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23583 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ICUSTAY_ID           DEATHTIME              INTIME\n",
       "10         200038                 NaT 2143-10-24 20:35:24\n",
       "11         200040                 NaT 2153-10-24 16:01:41\n",
       "12         200049                 NaT 2118-08-28 08:56:44\n",
       "13         200050                 NaT 2149-07-14 17:51:18\n",
       "14         200053                 NaT 2166-02-27 18:45:49\n",
       "15         200055                 NaT 2179-03-17 09:59:19\n",
       "16         200061                 NaT 2134-01-23 16:38:46\n",
       "17         200063                 NaT 2141-03-09 23:20:49\n",
       "18         200067                 NaT 2126-06-29 18:45:37\n",
       "19         200069                 NaT 2195-12-12 02:12:35\n",
       "20         200072                 NaT 2106-03-03 19:39:49\n",
       "21         200075                 NaT 2159-09-23 00:13:20\n",
       "22         200079                 NaT 2158-01-02 20:54:10\n",
       "23         200081                 NaT 2142-03-01 16:46:10\n",
       "24         200087                 NaT 2196-08-30 11:19:49\n",
       "25         200094                 NaT 2198-06-21 12:27:37\n",
       "26         200095 2113-10-31 00:20:00 2113-10-27 15:23:21\n",
       "27         200098                 NaT 2136-03-27 11:50:32\n",
       "28         200099                 NaT 2163-06-21 19:49:09\n",
       "29         200104                 NaT 2198-02-24 12:33:19\n",
       "30         200105 2104-10-23 00:01:00 2104-10-23 18:30:01\n",
       "31         200108                 NaT 2110-12-10 01:18:17\n",
       "32         200116                 NaT 2198-03-19 20:16:11\n",
       "33         200126                 NaT 2126-12-14 11:29:28\n",
       "34         200131                 NaT 2176-10-30 12:05:18\n",
       "35         200133                 NaT 2172-02-14 14:53:10\n",
       "36         200135                 NaT 2145-10-12 18:46:47\n",
       "37         200138                 NaT 2190-07-28 19:41:12\n",
       "38         200141                 NaT 2143-10-15 11:24:29\n",
       "39         200143 2191-04-28 14:31:00 2191-04-01 21:45:49\n",
       "...           ...                 ...                 ...\n",
       "23590      299867 2186-08-02 02:31:00 2186-07-15 16:55:40\n",
       "23591      299871                 NaT 2170-02-02 22:33:55\n",
       "23592      299872                 NaT 2192-02-22 14:52:52\n",
       "23593      299875                 NaT 2155-06-05 15:59:09\n",
       "23594      299879                 NaT 2174-08-15 09:23:17\n",
       "23595      299880                 NaT 2159-06-02 20:04:41\n",
       "23596      299883                 NaT 2165-05-24 17:07:19\n",
       "23597      299889 2172-06-09 04:30:00 2172-05-30 09:05:45\n",
       "23598      299898                 NaT 2169-09-16 04:29:52\n",
       "23599      299901                 NaT 2186-11-26 17:23:47\n",
       "23600      299904                 NaT 2187-01-21 00:25:29\n",
       "23601      299909                 NaT 2182-04-25 04:08:35\n",
       "23602      299913                 NaT 2121-07-21 02:08:50\n",
       "23603      299914                 NaT 2141-08-16 23:08:56\n",
       "23604      299921                 NaT 2103-05-08 09:43:51\n",
       "23605      299923 2158-12-13 17:36:00 2158-12-13 13:14:38\n",
       "23606      299928                 NaT 2137-11-06 17:23:04\n",
       "23607      299929                 NaT 2145-01-14 22:33:54\n",
       "23608      299930                 NaT 2194-07-04 12:50:24\n",
       "23609      299933                 NaT 2105-08-03 14:40:37\n",
       "23610      299943                 NaT 2151-08-25 03:29:06\n",
       "23611      299947                 NaT 2116-11-27 21:10:23\n",
       "23612      299948                 NaT 2119-05-25 13:47:31\n",
       "23613      299949                 NaT 2118-07-16 05:42:45\n",
       "23614      299950                 NaT 2122-06-20 13:25:29\n",
       "23615      299956                 NaT 2177-05-29 07:38:54\n",
       "23616      299957                 NaT 2132-10-13 10:41:17\n",
       "23617      299962                 NaT 2195-11-25 19:04:06\n",
       "23618      299979                 NaT 2127-12-05 02:16:42\n",
       "23619      299998                 NaT 2181-07-05 18:47:40\n",
       "\n",
       "[23583 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examples[['ICUSTAY_ID', 'DEATHTIME', 'INTIME']].iloc[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Death time < intime\n",
    "sum((examples['DEATHTIME'] - examples['INTIME']).dt.total_seconds() < 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.359300e+04\n",
       "mean    -7.175857e-06\n",
       "std      6.949819e-04\n",
       "min     -1.200000e-03\n",
       "25%     -6.111111e-04\n",
       "50%     -7.105427e-15\n",
       "75%      5.888889e-04\n",
       "max      1.188889e-03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOS vs OUTTIME - INTIME\n",
    "((examples['OUTTIME'] - examples['INTIME']).dt.total_seconds() / 3600 - examples['LOS']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((examples['DEATHTIME'] - examples['INTIME']).dt.total_seconds() / 3600).hist(bins=np.arange(-5, 200, 1), alpha=0.9)\n",
    "plt.xlabel('Survival time (h)')\n",
    "plt.ylabel('Number of ICU stays')\n",
    "plt.text(80, 30, 'Death <= 200h: {}\\nDeath > 200h: {} (not shown) \\nDeath = null: {} (not shown)' \\\n",
    "         .format(sum(((examples['DEATHTIME'] - examples['INTIME']).dt.total_seconds() / 3600) <= 200), \n",
    "                 sum(((examples['DEATHTIME'] - examples['INTIME']).dt.total_seconds() / 3600) > 200),\n",
    "                sum(examples['DEATHTIME'].isnull())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23593"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examples['ICUSTAY_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21439, 2154)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum((examples['LOS']) <= 200), sum((examples['LOS']) > 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(examples['LOS']).hist(bins=np.arange(-5, 200, 1), alpha=0.9)\n",
    "plt.xlabel('Length of stay (h)')\n",
    "plt.ylabel('Number of ICU stays')\n",
    "plt.text(80, 500, 'LOS <= 200h: {}\\nLOS > 200h: {} (not shown)'.format(sum((examples['LOS']) <= 200), sum((examples['LOS']) > 200)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove died before cutoff hour\n",
    "examples = examples[(examples.DEATHTIME >= examples.INTIME + timedelta(hours=cutoff_h)) | (examples.DEATHTIME.isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove LOS < cutoff hour\n",
    "examples = examples[examples['LOS'] >= cutoff_h]"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}