clinical_app_starter.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOTyXtFYH4nf"
      },
      "source": [
        "#### Load the dataset\n",
        "\n",
        "The dataset is stored as [.npz](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) files. Each file contains roughly 24 hours of heart rate data in the 'hr' array sampled at 1Hz. The subject ID is the name of the file. \n",
        "\n",
        "Demographics metadata is stored in a file called 'metadata.csv'. This CSV has three columns, one for subject ID, age group, and sex. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kk4tB23AH4ng"
      },
      "outputs": [],
      "source": [
        "hr_filenames = glob.glob('/data/crisdb/*.npz')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jA1WIewH4ng"
      },
      "source": [
        "#### Load Metadata\n",
        "Load the metadata file into a datastructure that allows for easy lookups from subject ID to age group and sex."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4doO0OQ2H4ng"
      },
      "outputs": [],
      "source": [
        "metadata_filename = '/data/crisdb/metadata.csv'\n",
        "\n",
        "# Load the metadata file into this variable.\n",
        "with open(metadata_filename, 'r') as f:\n",
        "    metadata = pd.read_csv(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7AxzwTVgH4nh",
        "outputId": "91dceadf-2d98-4198-fafc-61250064d40d"
      },
      "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>subject</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e198a</td>\n",
              "      <td>20-24</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e198b</td>\n",
              "      <td>20-24</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e028b</td>\n",
              "      <td>30-34</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e028a</td>\n",
              "      <td>30-34</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e061b</td>\n",
              "      <td>30-34</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  subject    age   sex\n",
              "0   e198a  20-24  Male\n",
              "1   e198b  20-24  Male\n",
              "2   e028b  30-34  Male\n",
              "3   e028a  30-34  Male\n",
              "4   e061b  30-34  Male"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metadata.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Yp9nD4EH4nh"
      },
      "source": [
        "#### Compute Resting Heart Rate\n",
        "For each subject we want to compute the resting heart rate while keeping track of which age group this subject belongs to. An easy, robust way to compute the resting heart rate is to use the lowest 5th percentile value in the heart rate timeseries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BOlvfWPHH4ni"
      },
      "outputs": [],
      "source": [
        "def AgeAndRHR(metadata, filename):\n",
        "\n",
        "    # Load the heart rate timeseries\n",
        "    hr_data = np.load(filename)['hr']\n",
        "    \n",
        "    # Compute the resting heart rate from the timeseries by finding the lowest 5th percentile value in hr_data\n",
        "    rhr = np.percentile(hr_data, 5)\n",
        "\n",
        "    # Find the subject ID from the filename.\n",
        "    subject = filename.split(os.sep)[-1].split(\".\")[0]\n",
        "    \n",
        "    # Find the age group for this subject in metadata.\n",
        "    age_group = metadata[metadata['subject']==subject]['age'].values[0]\n",
        "    \n",
        "    # Find the sex for this subject in metadata.\n",
        "    sex = metadata[metadata['subject']==subject]['sex'].values[0]\n",
        "\n",
        "    return age_group, sex, rhr\n",
        "\n",
        "df = pd.DataFrame(data=[AgeAndRHR(metadata, filename) for filename in hr_filenames],\n",
        "                  columns=['age_group', 'sex', 'rhr'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_qTCs7UcH4ni",
        "outputId": "5633835d-8733-436f-d2df-28e2af0c0c89"
      },
      "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>age_group</th>\n",
              "      <th>sex</th>\n",
              "      <th>rhr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60-64</td>\n",
              "      <td>Female</td>\n",
              "      <td>89.302326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65-69</td>\n",
              "      <td>Male</td>\n",
              "      <td>65.641026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45-49</td>\n",
              "      <td>Male</td>\n",
              "      <td>56.928458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60-64</td>\n",
              "      <td>Male</td>\n",
              "      <td>68.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50-54</td>\n",
              "      <td>Male</td>\n",
              "      <td>84.395604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  age_group     sex        rhr\n",
              "0     60-64  Female  89.302326\n",
              "1     65-69    Male  65.641026\n",
              "2     45-49    Male  56.928458\n",
              "3     60-64    Male  68.571429\n",
              "4     50-54    Male  84.395604"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6f909i2H4ni"
      },
      "source": [
        "#### Plot Resting Heart Rate vs. Age Group\n",
        "We'll use [seaborn](https://seaborn.pydata.org/) to plot the relationship. Seaborn is a thin wrapper around matplotlib, which we've used extensively in this class, that enables higher-level statistical plots.\n",
        "\n",
        "We will use [lineplot](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn.lineplot) to plot the mean of the resting heart rates for each age group along with the 95% confidence interval around the mean. Learn more about making plots that show uncertainty [here](https://seaborn.pydata.org/tutorial/relational.html#aggregation-and-representing-uncertainty)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eduNG9OVH4nj",
        "outputId": "fd6cb623-ba10-4df8-dd18-c1cbc2cfc7df"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f777aba7550>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "labels = sorted(np.unique(df.age_group))\n",
        "df['xaxis'] = df.age_group.map(lambda x: labels.index(x)).astype('float')\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.lineplot(x='xaxis', y='rhr', hue='sex', data=df)\n",
        "_ = plt.xticks(np.arange(len(labels)), labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BefcpsQyH4nj",
        "outputId": "115c4621-f067-4dbc-9c1f-de8ab1ec1777"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f572f8fc898>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "order = pd.Series(df['age_group'].unique()).sort_values()\n",
        "sns.countplot(data=df, x='age_group', order=order, hue = 'sex');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWr3f39rH4nk"
      },
      "source": [
        "1. For women, we see a higher fluctuation of the resting heart rates, we can observe a peak for women in age 40-44 and the smallest value for 70-74 years old.\n",
        "\n",
        "2. For men we don't see a lot of fluctuations of the resting heart rates it seems roughly constant, and the confidence interval is smaller.\n",
        "\n",
        "3. In comparison to men, women's heart rate is higher on average while resting.\n",
        "\n",
        "4. The number of men is much bigger than women, so it could be not representative. The other possible reason could be that men are less overloaded than women even when resting they could be calmer, but this is very arguable.\n",
        "\n",
        "5. We should have more personal information about the outlook of these people, their habits and what kind of work they are doing for example to have a better understanding.\n",
        "\n",
        "6. From the plot, one can observe the trend that the average resting heart rate for women increases untill 40 years and then decreases steadily, but the sample is too \n",
        "6 - From the plot, one can see the trend that the average resting heart rate for women increases until 40 and then decreases, but the sample is too small to make such suggestions."
      ]
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