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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

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/nature21056Gulshan 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

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.

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, 2017Pollard, 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

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.5870Podcast

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