Papers discussed in this Section 4 podcast:

• Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah. Improving Palliative Care with Deep Learning. arXiv:1711.06402
• Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, Bhatt DL, Fonarow GC, Laskey WK. Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure Comparison of Machine Learning and Other Statistical Approaches. JAMA Cardiol. 2017;2(2):204–209. doi:10.1001/jamacardio.2016.3956
• Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller. An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. arXiv:1708.05894
• Riccardo Miotto, Li Li, Brian A. Kidd & Joel T. Dudley. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports 6, Article number: 26094 (2016) doi:10.1038/srep26094

Podcast Contents:

• Why These Papers?
• Predict 30 day all cause readmission
  • How I was surprised.
  • Appreciation for data inputs.
  • Improving the classification
    • Better representation through deep learning.
    • Consider time rather than a snapshot of a given admission.
    • Consider severity of the diseases.
    • Consider medication dosages as a proxy for disease severity.
• Palliative Care
  • Observation Windows
  • Area under the Precision Recall  Curve.
  • The target is a proxy.
  • Model explanation.
• Deep patient
  • Building good features.
  • Dealing with noisy data.
  • Sparsity in the number of notes per patient.
  • Sparsity in the number of patients with a feature.
  • Topic Modeling.
  • ICD-9 Granularity.
  • Tools
    • Open Biomedical Annotator
• Early Sepsis
  • Undefined time zero.
  • Dealing with time series.
  • irregularly spaced recording.
  • Informed missingness.
  • Case control matching.
  • Matched lookback.
  • Realtime validation.
• Student Questions