{
  "fetched_at": "2026-10-20T09:32:19Z",
  "source_url": "https://www.kaggle.com/competitions/deephealth-1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\r\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\r\n<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>train.csv</strong> - the training set</li>\r\n<li><strong>test.csv</strong> - the test set</li>\r\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\r\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\r\n<li>etc...</li>\r\n</ul>\r\n<h2></h2>\r\n<ul>\r\n<li></li>\r\n</ul>",
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    {
      "name": "Description",
      "content": "<p>The amount of medical data that has been collected and saved over the past few decades, coupled with the recent advancement of Machine Learning and Artificial Intelligence presents an interesting question; how can we interpolate medical data using machine learning to produce useful insights? The Deep Health Hackathon aims to tackle that problem.</p>\r\n<p>Over the course of 48 hours, participants of this hackathon will implement machine learning algorithms on medical data – provided by MedChart – to develop baselines as well as produce new insights into what this data is really telling us. Each team will be responsible for pitching their results to a panel of judges, where a winning team will be selected.</p>\r\n<h1>The competition</h1>\r\n<p>Competitors will be provided with data and must generate insights from it using a language of their choice in teams of 3-6. A more specific outline will be given at the competition</p>\r\n<h3>Who can participate?</h3>\r\n<p>All students are welcome to participate, including those not from the University of Toronto. To participate in the Hatchery, teams must have at least 2 students must be from U of T. If you are not a student, you can still participate as a mentor, reach out to us before the competition!</p>\r\n<h3>What do I pay?</h3>\r\n<p>The competition has a $10 registration fee that will cover your meals for the day, but we do not offer travel reimbursement.<br />My major is healthcare, but I can't code, can I participate?<br />Yes! Our goal is to form multidisciplinary teams where you can advise coders on the relevancy of their findings. We will also be offering workshops on machine learning.</p>\r\n<h3>Prizes</h3>\r\n<p>The top three teams will be presented with the opportunity to join the 2017 Cohort with the Hatchery. An additional $500 dollars will be rewarded to the top team courtesy of General Electric and are guaranteed an interview for a machine learning position at Medchart. Additional prizes will be provided to top competitors.</p>",
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    {
      "name": "Evaluation",
      "content": "<hr />\r\n<h2>Submission Format</h2>\r\n<p>See the sample_solution.csv file for the submission format</p>",
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    },
    {
      "name": "rules",
      "content": "<p>N/A</p>",
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