R25 VOICE Section 4 - ExEmplar Clinical Machine Learning R25 VOICE
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- Kurse
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:1711.06402Frizzell 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.2016.3956Joseph 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:1708.05894Riccardo 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/srep26094Podcast Contents:
Why These Papers?Predict 30 day all cause readmissionHow I was surprised.Appreciation for data inputs.Improving the classificationBetter 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 CareObservation WindowsArea under the Precision Recall Curve.The target is a proxy.Model explanation.Deep patientBuilding 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.ToolsOpen Biomedical AnnotatorEarly SepsisUndefined time zero.Dealing with time series.irregularly spaced recording.Informed missingness.Case control matching.Matched lookback.Realtime validation.Student Questions
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:1711.06402Frizzell 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.2016.3956Joseph 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:1708.05894Riccardo 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/srep26094Podcast Contents:
Why These Papers?Predict 30 day all cause readmissionHow I was surprised.Appreciation for data inputs.Improving the classificationBetter 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 CareObservation WindowsArea under the Precision Recall Curve.The target is a proxy.Model explanation.Deep patientBuilding 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.ToolsOpen Biomedical AnnotatorEarly SepsisUndefined time zero.Dealing with time series.irregularly spaced recording.Informed missingness.Case control matching.Matched lookback.Realtime validation.Student Questions