notebooks/intakeoutput.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# intakeOutput\n",
    "\n",
    "Intake and output recorded for patients. Entered from the nursing flowsheet (either manually or interfaced into the hospital system)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "import pdvega\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine a single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "patientunitstayid = 242380"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>intakeoutputid</th>\n",
       "      <th>intakeoutputyear</th>\n",
       "      <th>intakeoutputtime24</th>\n",
       "      <th>intakeoutputtime</th>\n",
       "      <th>intakeoutputoffset</th>\n",
       "      <th>intaketotal</th>\n",
       "      <th>outputtotal</th>\n",
       "      <th>dialysistotal</th>\n",
       "      <th>nettotal</th>\n",
       "      <th>intakeoutputentryyear</th>\n",
       "      <th>intakeoutputentrytime24</th>\n",
       "      <th>intakeoutputentrytime</th>\n",
       "      <th>intakeoutputentryoffset</th>\n",
       "      <th>cellpath</th>\n",
       "      <th>celllabel</th>\n",
       "      <th>cellvaluenumeric</th>\n",
       "      <th>cellvaluetext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>242380</td>\n",
       "      <td>17914026</td>\n",
       "      <td>2015</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>noon</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-650.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:08:18</td>\n",
       "      <td>evening</td>\n",
       "      <td>127</td>\n",
       "      <td>flowsheet|Flowsheet Cell Labels|I&amp;O|Weight|Bod...</td>\n",
       "      <td>Bodyweight (lb)</td>\n",
       "      <td>269.0</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>242380</td>\n",
       "      <td>17914024</td>\n",
       "      <td>2015</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-650.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:08:18</td>\n",
       "      <td>evening</td>\n",
       "      <td>127</td>\n",
       "      <td>flowsheet|Flowsheet Cell Labels|I&amp;O|Output (ml...</td>\n",
       "      <td>Urine</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>242380</td>\n",
       "      <td>17914025</td>\n",
       "      <td>2015</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-181</td>\n",
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       "      <td>650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-650.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:08:18</td>\n",
       "      <td>evening</td>\n",
       "      <td>127</td>\n",
       "      <td>flowsheet|Flowsheet Cell Labels|I&amp;O|Weight|Bod...</td>\n",
       "      <td>Bodyweight (kg)</td>\n",
       "      <td>122.0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>242380</td>\n",
       "      <td>17609931</td>\n",
       "      <td>2015</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>night</td>\n",
       "      <td>299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>21:01:57</td>\n",
       "      <td>night</td>\n",
       "      <td>420</td>\n",
       "      <td>flowsheet|Flowsheet Cell Labels|I&amp;O|Output (ml...</td>\n",
       "      <td>Urine</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>242380</td>\n",
       "      <td>18175773</td>\n",
       "      <td>2015</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07:54:33</td>\n",
       "      <td>midday</td>\n",
       "      <td>1073</td>\n",
       "      <td>flowsheet|Flowsheet Cell Labels|I&amp;O|Output (ml...</td>\n",
       "      <td>Urine</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
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      "text/plain": [
       "   patientunitstayid  intakeoutputid  intakeoutputyear intakeoutputtime24  \\\n",
       "0             242380        17914026              2015           11:00:00   \n",
       "1             242380        17914024              2015           11:00:00   \n",
       "2             242380        17914025              2015           11:00:00   \n",
       "3             242380        17609931              2015           19:00:00   \n",
       "4             242380        18175773              2015           03:00:00   \n",
       "\n",
       "  intakeoutputtime  intakeoutputoffset  intaketotal  outputtotal  \\\n",
       "0             noon                -181          0.0        650.0   \n",
       "1             noon                -181          0.0        650.0   \n",
       "2             noon                -181          0.0        650.0   \n",
       "3            night                 299          0.0        500.0   \n",
       "4          morning                 779          0.0        700.0   \n",
       "\n",
       "   dialysistotal  nettotal  intakeoutputentryyear intakeoutputentrytime24  \\\n",
       "0            0.0    -650.0                   2015                16:08:18   \n",
       "1            0.0    -650.0                   2015                16:08:18   \n",
       "2            0.0    -650.0                   2015                16:08:18   \n",
       "3            0.0    -500.0                   2015                21:01:57   \n",
       "4            0.0    -700.0                   2015                07:54:33   \n",
       "\n",
       "  intakeoutputentrytime  intakeoutputentryoffset  \\\n",
       "0               evening                      127   \n",
       "1               evening                      127   \n",
       "2               evening                      127   \n",
       "3                 night                      420   \n",
       "4                midday                     1073   \n",
       "\n",
       "                                            cellpath        celllabel  \\\n",
       "0  flowsheet|Flowsheet Cell Labels|I&O|Weight|Bod...  Bodyweight (lb)   \n",
       "1  flowsheet|Flowsheet Cell Labels|I&O|Output (ml...            Urine   \n",
       "2  flowsheet|Flowsheet Cell Labels|I&O|Weight|Bod...  Bodyweight (kg)   \n",
       "3  flowsheet|Flowsheet Cell Labels|I&O|Output (ml...            Urine   \n",
       "4  flowsheet|Flowsheet Cell Labels|I&O|Output (ml...            Urine   \n",
       "\n",
       "   cellvaluenumeric cellvaluetext  \n",
       "0             269.0           269  \n",
       "1             650.0           650  \n",
       "2             122.0           122  \n",
       "3             500.0           500  \n",
       "4             700.0           700  "
      ]
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     "execution_count": 11,
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   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select *\n",
    "from intakeoutput\n",
    "where patientunitstayid = {}\n",
    "order by intakeoutputoffset\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see that the type of data recorded is described by the `cellpath`. `cellpath` is hierarchical, with pipes (`|`) separating hierarchies. As expected, most data here will fall under the I&O hierarchy. We can see the patient body weight is recorded in both pounds (lbs) and kilograms (kg). The patient's urine output is also documented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urine output\n",
    "\n",
    "Though not recommended for actual use in a study, we can write a query to quickly get an idea of urine output for this patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {
      "jupyter-vega3": "#5843d9c3-5788-47c6-8aca-aa302ebb5ab9"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_uo = df.loc[df['celllabel'].str.contains('Urine'), :].copy()\n",
    "df_uo['uo'] = pd.to_numeric(df_uo['cellvaluenumeric'], errors='coerce')\n",
    "df_uo['uo'] = df_uo['uo'].cumsum()\n",
    "\n",
    "cols = ['uo']\n",
    "df_uo.set_index('intakeoutputoffset')[cols].vgplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General intake/output\n",
    "\n",
    "The columns `intaketotal`, `outputtotal`, `dialysistotal`, and `nettotal` give us an easy way to plot the patient's fluid balance over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"1b6d3307-c4fe-44c7-a5f2-956069828857\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#1b6d3307-c4fe-44c7-a5f2-956069828857"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"selection\": {\"grid\": {\"type\": \"interval\", \"bind\": \"scales\"}}, \"mark\": \"line\", \"data\": {\"values\": [{\"intakeoutputoffset\": -181, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 299, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 779, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 1259, \"variable\": \"intaketotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"outputtotal\", \"value\": 650.0}, {\"intakeoutputoffset\": -181, \"variable\": \"outputtotal\", \"value\": 650.0}, {\"intakeoutputoffset\": -181, \"variable\": \"outputtotal\", \"value\": 650.0}, {\"intakeoutputoffset\": 299, \"variable\": \"outputtotal\", \"value\": 500.0}, {\"intakeoutputoffset\": 779, \"variable\": \"outputtotal\", \"value\": 700.0}, {\"intakeoutputoffset\": 1259, \"variable\": \"outputtotal\", \"value\": 1900.0}, {\"intakeoutputoffset\": -181, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 299, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 779, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": 1259, \"variable\": \"dialysistotal\", \"value\": 0.0}, {\"intakeoutputoffset\": -181, \"variable\": \"nettotal\", \"value\": -650.0}, {\"intakeoutputoffset\": -181, \"variable\": \"nettotal\", \"value\": -650.0}, {\"intakeoutputoffset\": -181, \"variable\": \"nettotal\", \"value\": -650.0}, {\"intakeoutputoffset\": 299, \"variable\": \"nettotal\", \"value\": -500.0}, {\"intakeoutputoffset\": 779, \"variable\": \"nettotal\", \"value\": -700.0}, {\"intakeoutputoffset\": 1259, \"variable\": \"nettotal\", \"value\": -1900.0}]}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.json\", \"height\": 300, \"encoding\": {\"y\": {\"field\": \"value\", \"type\": \"quantitative\"}, \"x\": {\"field\": \"intakeoutputoffset\", \"type\": \"quantitative\"}, \"color\": {\"field\": \"variable\", \"type\": \"nominal\"}}};\n",
       "var selector = \"#1b6d3307-c4fe-44c7-a5f2-956069828857\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#1b6d3307-c4fe-44c7-a5f2-956069828857"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega3": "#1b6d3307-c4fe-44c7-a5f2-956069828857"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['intaketotal', 'outputtotal', 'dialysistotal', 'nettotal']\n",
    "df.set_index('intakeoutputoffset')[cols].vgplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, it is unlikely that the patient has good urine output for almost 20 hours with no corresponding fluid intake - and even less likely that urine output is the *only* factor affecting patient fluid balance and likely the `nettotal` column is a naive aggregation of information documented in the intakeOutput table. Indeed, we can see from the infusionDrug table that the patient is receiving both heparin and nitroglycerin, which should be factored in as inputs when calculating patient fluid balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>data completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>7059</td>\n",
       "      <td>6914</td>\n",
       "      <td>97.945885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>5666</td>\n",
       "      <td>93.007223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>264</td>\n",
       "      <td>5237</td>\n",
       "      <td>5088</td>\n",
       "      <td>97.154860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>4446</td>\n",
       "      <td>95.020303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>4232</td>\n",
       "      <td>98.947861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>4156</td>\n",
       "      <td>97.949564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>199</td>\n",
       "      <td>4240</td>\n",
       "      <td>3970</td>\n",
       "      <td>93.632075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>176</td>\n",
       "      <td>4328</td>\n",
       "      <td>3935</td>\n",
       "      <td>90.919593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>458</td>\n",
       "      <td>3701</td>\n",
       "      <td>3663</td>\n",
       "      <td>98.973250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>300</td>\n",
       "      <td>3617</td>\n",
       "      <td>3534</td>\n",
       "      <td>97.705281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "11           73                7059                         6914   \n",
       "54          167                6092                         5666   \n",
       "106         264                5237                         5088   \n",
       "184         420                4679                         4446   \n",
       "134         338                4277                         4232   \n",
       "90          243                4243                         4156   \n",
       "71          199                4240                         3970   \n",
       "58          176                4328                         3935   \n",
       "206         458                3701                         3663   \n",
       "122         300                3617                         3534   \n",
       "\n",
       "     data completion  \n",
       "11         97.945885  \n",
       "54         93.007223  \n",
       "106        97.154860  \n",
       "184        95.020303  \n",
       "134        98.947861  \n",
       "90         97.949564  \n",
       "71         93.632075  \n",
       "58         90.919593  \n",
       "206        98.973250  \n",
       "122        97.705281  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct a.patientunitstayid) as number_of_patients_with_tbl\n",
    "from patient pt\n",
    "left join intakeoutput a\n",
    "  on pt.patientunitstayid = a.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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"
     },
     "metadata": {
      "jupyter-vega3": "#165e2fc6-a69a-49c7-88c1-a30f3ac4e112"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['data completion']].vgplot.hist(bins=10,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see that around 10 hospitals have very few to no patients documented in the intakeOutput table (left side of histogram, 0-10% bin), while over 120 hospitals have 90-100% of patients with data in intakeOutput (right side of histogram, 0-90% bin). "
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}