{
  "id": "1712.00563",
  "version": "v1",
  "published": "2017-12-02T07:27:28.000Z",
  "updated": "2017-12-02T07:27:28.000Z",
  "title": "Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning",
  "authors": [
    "Gabriel Erion",
    "Hugh Chen",
    "Scott M. Lundberg",
    "Su-In Lee"
  ],
  "comment": "To be presented at Machine Learning for Health Workshop: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA",
  "categories": [
    "cs.LG",
    "stat.AP",
    "stat.ML"
  ],
  "abstract": "We use a deep learning model trained only on a patient's blood oxygenation data (measurable with an inexpensive fingertip sensor) to predict impending hypoxemia (low blood oxygen) more accurately than trained anesthesiologists with access to all the data recorded in a modern operating room. We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values. This work has the potential to provide cutting-edge clinical decision support in low-resource settings, where rates of surgical complication and death are substantially greater than in high-resource areas.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-02T07:27:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "spo2 data",
      "anesthesiologist-level forecasting",
      "patients past blood oxygen values",
      "patients blood oxygenation data"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}