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
- Early Sepsis
- Undefined time zero.
- Dealing with time series.
- irregularly spaced recording.
- Informed missingness.
- Case control matching.
- Matched lookback.
- Realtime validation.
- Student Questions