Papers discussed in this Section 6 Podcast:

• H. Salehinejad, J. Barfett, S. Valaee, E. Colak, A. Mnatzakanian, and T. Dowdell. Interpretation of mammogram and chest radiograph reports using deep neural networks-preliminary results. arXiv preprint arXiv:1708.09254, 2017.
• Hassanpour S, Langlotz CP. Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning. Acad Radiol 2016; 23 (01) 84-89.
• Pons E., Braun L.M.M., Hunink M.G.M. et al. (2016) Natural language processing in radiology: a systematic review. Radiology, 279, 329–343.
• Trivedi, H., Mesterhazy, J., Laguna, B. et al. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm. J Digit Imaging (2017). https://doi.org/10.1007/s10278-017-0021-3

Podcast Contents

• Why These Papers
• NLP Review
  • Defining NLP
  • NLP Pipeline in Figure 1
  • Radlex
  • Evaluation Measures - F1
  • Types
    • Diagnostic Surveillance
    • Cohort Building
    • Query based case retrieval
    • Quality Assessment in radiologic practice
    • Communication of critical results
    • Clinical Support Services
  • Resources in Table 2
  • Operational Barriers
  • Future Research Needs
• IV Contrast
  • Why Chosen?
  • Notes
    • Processing Time Discussion
    • Error analysis
    • Cloud Service
    • Passive Workflow integration.
• Predicting High Imaging Utilization
  • Why Chosen?
  • Notes
    • SVM usage.
    • Document-Feature Matrix
    • Overfit
• Interpretation of Mammograms
  • Why Chosen?
  • Notes
    • Bi-directional CNN
    • Passive Workflow Integration
    • Preprocessing
• Why Deep Learning
• Questions

Papers discussed in this Section 5 Podcast:

• Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (02 February 2017) doi:10.1038/nature21056
• Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
• Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:1711.05225

Podcast Contents

• Why These Papers?
• Dermatology Paper
  • Concepts
    • Inception v3
    • Pretraining
    • t-SNE
    • Comparison to humans
  • Combining clinical data with imaging
• CheXnet
  • Concepts
    • Densenet
    • Pretraining
    • Horizontal flipping
    • Class Activation Mappings
  • Implications of downscaling
• Retinopathy Paper
  • Human Comparison
  • Different Cameras
  • Concepts
    • Pretraining
    • Multitask -single network, multiple outputs
    • Early stopping criteria
    • Ensemble
    • Learning Curves
  • What is the Model Learning?

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

Papers discussed in this Section 3 podcast:

• Liao, Fangzhou; Liang, Ming; Li, Zhe; Hu, Xiaolin; and Song, Sen. Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network. eprint arXiv:1711.08324, 2017
• Pollard, T. J., & Johnson, A. E. W. The MIMIC-III Clinical Database. http://dx.doi.org/10.13026/C2XW26 (2016)
• Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, and Andrew Ng. MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs. arXiv:1712.06957, 2017
• X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR (spotlight);  arXiv:1705.02315, 2017

Podcast Contents:

• Why Datasets are important?
• Kinds of Datasets?
• What's a gold standard?
• Best practices in dataset descriptions.
  • Sample distribution
  • Meta-data
    • Patients
    • Radiologists
    • PACS Systems Used for Annotation
    • Images
• Strategies for Labeling Data
  • Natural Language Processing
  • Amazon Mechanical Turk
  • Natural Language Processing Validation Sets 

Papers discussed in this Section 2 podcast:

• Domingos, Pedro. 2012. “A Few Useful Things to Know about Machine Learning.” Communications of the ACM 55 (10):78.
• Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, Venkatesh S, Berk M “Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View” J Med Internet Res 2016;18(12):e323 DOI: 10.2196/jmir.5870

Podcast Contents:

• Generalization
• Overfitting
• Feature Engineering
• Improving model performance
  • More data
  • Better algorithms
  • Ensembling
• Review of checklists for writing machine learning papers.
• Student questions
  • Knowledge vs Data
  • JMIR reputation
  • Informatics journals and computer science proceedings
  • Sample size for good classifier performance.

In this podcast, we discuss important themes from 3 papers and answer student generated questions. The papers we review are:

Chen, Jonathan H., and Steven M. Asch. 2017. “Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations.” The New England Journal of Medicine 376 (26):2507–9.

Erickson, Bradley J., Panagiotis Korfiatis, Zeynettin Akkus, and Timothy L. Kline. 2017. “Machine Learning for Medical Imaging.” Radiographics: A Review Publication of the Radiological Society of North America, Inc 37 (2):505–15.

Obermeyer, Ziad, and Ezekiel J. Emanuel. 2016. “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine.” The New England Journal of Medicine 375 (13):1216–19.