jill-cates-how-to-build-a-clinical-diagnostic-model-in-python-pycon-2019.json

{
  "copyright_text": null,
  "description": "Diagnosing a patient requires consideration of a wide variety of\nfactors: past medical history and comorbidities, physical exam findings,\nlab results, imaging, ECG findings, and in some cases, genomic testing.\nClinical diagnosis and prognostic assessment currently relies on expert\nknowledge of the treating physician. Recent developments in machine\nlearning make it possible to build automated clinical diagnostic and\nrisk assessment tools using data from the electronic medical record.\n\nThis talk walks through the steps involved in building a clinical risk\nassessment model, using sepsis as a case study. A large part of the talk\nwill focus on the tools and techniques involved in pre-processing\ncomplex medical data, and strategies for evaluating model results.\n",
  "duration": 1507,
  "language": "eng",
  "recorded": "2019-05-03T17:10:00",
  "related_urls": [
    {
      "label": "Conference schedule",
      "url": "https://us.pycon.org/2019/schedule/talks/"
    },
    {
      "label": "Conference slides (github)",
      "url": "https://github.com/PyCon/2019-slides"
    },
    {
      "label": "Conference slides (speakerdeck)",
      "url": "https://speakerdeck.com/pycon2019"
    },
    {
      "label": "Talk schedule",
      "url": "https://us.pycon.org/2019/schedule/presentation/173/"
    }
  ],
  "speakers": [
    "Jill Cates"
  ],
  "tags": [
    "talk"
  ],
  "thumbnail_url": "https://i.ytimg.com/vi/9J83Pp9qH5w/maxresdefault.jpg",
  "title": "How to Build a Clinical Diagnostic Model in Python",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=9J83Pp9qH5w"
    }
  ]
}