Agenda

Week 1 / Jan 16, 2019

Orientation and welcome slides

Week 2 / Jan 23, 2019

• Topol, Eric J. “High-performance medicine: the convergence of human and artificial intelligence.” Nature Medicine 25.1 (2019): 44. link
• Challen, Robert, et al. “Artificial intelligence, bias and clinical safety” BMJ Qual Saf Published Online First: 12 January 2019. link

Week 3 / Jan 30, 2019

• (School closed due to the polar vortex - rescheduled for week 15)
• Lippert, Christoph, et al. “Identification of individuals by trait prediction using whole-genome sequencing data.” Proceedings of the National Academy of Sciences 114.38 (2017): 10166-10171. paper and supporting materials and response and response to response.

Week 4 / Feb 6, 2019

• Esteva, Andre, et al. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature 542.7639 (2017): 115-118. link
• (Optional review) Haenssle, H. A., et al. “Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.” Annals of Oncology (2018). link
• Gulshan, Varun, et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.” Jama 316.22 (2016): 2402-2410. link
• (Optional review) Poplin, Ryan, et al. “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” Nature Biomedical Engineering 2.3 (2018): 158. link

Week 5 / Feb 13, 2019 / Project proposals due

• Miotto, Riccardo, et al. “Deep patient: an unsupervised representation to predict the future of patients from the electronic health records.” Scientific reports 6 (2016): 26094. link
• Rajkomar, Alvin, et al. “Scalable and accurate deep learning with electronic health records.” npj Digital Medicine 1.1 (2018): 18. link

Week 6 / Feb 20, 2019

• (Read - no review) Mincholé, Ana, and Blanca Rodriguez. “Artificial intelligence for the electrocardiogram.” Nature medicine 25.1 (2019): 22. link
• (Read - no review) Turakhia, Mintu P. “Moving From Big Data to Deep Learning—The Case of Atrial Fibrillation.” JAMA cardiology (2018). link
• Hannun, Awni Y., et al. “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.” Nature medicine 25.1 (2019): 65. link and website
• (optional review) Attia, Zachi I., et al. “Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram.” Nature medicine 25.1 (2019): 70. link
• (Skim) Tison, Geoffrey H., et al. “Passive detection of atrial fibrillation using a commercially available smartwatch.” JAMA cardiology (2018). link
• (Skim) Ballinger, Brandon, et al. “DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction.” AAAI Conference on Artificial Intelligence, Feb 2018 (AAAI-18) link
• (Skim) Halcox, Julian PJ, et al. “Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study.” Circulation 136.19 (2017): 1784-1794. link
• (Skim) Ansari, Sardar, et al. “A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction using Electrocardiogram and Electronic Health Records.” IEEE reviews in biomedical engineering 10 (2017): 264-298. link
• Zhang, Jeffrey, et al. “Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy.” Circulation 138.16 (2018): 1623-1635. link
• (optional review) Madani, Ali, et al. “Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease.” npj Digital Medicine 1.1 (2018): 59. link

Week 7 / Feb 27, 2019

• Akbari, Hassan, et al. “Towards reconstructing intelligible speech from the human auditory cortex.” Scientific reports 9.1 (2019): 874. link and website
• (Read - no review) Warraich, Haider J., et al. “The digital transformation of medicine can revitalize the patient-clinician relationship.” npj Digital Medicine 1.1 (2018): 49. link
• (Read - no review) Verghese, Abraham, et al. “What this computer needs is a physician: humanism and artificial intelligence.” Jama 319.1 (2018): 19-20. link
• Chiu, Chung-Cheng, et al. “Speech recognition for medical conversations.” arXiv preprint arXiv:1711.07274 (2017). link and Blog post
• (optional review) Al Hanai, Tuka, et al. “Detecting Depression with Audio/Text Sequence Modeling of Interviews.” Proc. Interspeech. 2018. link
• (optional review) Faurholt-Jepsen, Maria, et al. “Voice analysis as an objective state marker in bipolar disorder.” Translational psychiatry 6.7 (2016): e856. link
• (Skim) Wroge, Timothy J., et al. “Parkinson’s Disease Diagnosis Using Machine Learning and Voice.” 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2018. link
• (Skim) Victor, Ezekiel, et al. “Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation.” PsyArXiv. September 30 (2018). link
• (Skim) Coffey, Kevin R., et al. “DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations.” development 4 (2019): 21. link
• (Skim) Cummins, Nicholas, et al. “Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning.” Methods (2018).link

Week 8 / Mar 6, 2019 / Midterm report due

• (interact) 35 Years Of American Death. website
• (interact) Strava Global Heatmap. website
• (interact) HealthMap: Global Infectious Disease Monitoring. website
• (interact) Our World in Data - Global Health. website
• (interact) Data USA: Maps. website
• (interact) City Health Dashboard. website
• (interact) Toxmap: Toxic Chemicals in the US. website
• (interact) EPA’s EnviroAtlas. website
• (Read - no review) Ding, Ding. “Surveillance of global physical activity: progress, evidence, and future directions.” The Lancet Global Health 6.10 (2018). link
• Althoff, Tim, et al. “Large-scale physical activity data reveal worldwide activity inequality.” Nature 547.7663 (2017). link and website
• (optional review) Guthold, Regina, et al. “Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1· 9 million participants.” The Lancet Global Health 6.10 (2018). link
• (optional review) Chekroud, Sammi R., et al. “Association between physical exercise and mental health in 1· 2 million individuals in the USA between 2011 and 2015: a cross-sectional study.” The Lancet Psychiatry 5.9 (2018). link
• Cohen, Aaron J., et al. “Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.” The Lancet 389.10082 (2017). link
• (optional review) Foreman, Kyle J., et al. “Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories.” The Lancet 392.10159 (2018). link
• (Skim) Landrigan, Philip J., et al. “The Lancet Commission on pollution and health.” The Lancet 391.10119 (2018): 462-512. link

Week 9 / Mar 13, 2019

• (Read - no review) Muse, Evan D., et al. “Towards a smart medical home.” The Lancet 389.10067 (2017): 358. link
• (Read - no review) Gambhir, Sanjiv Sam, et al. “Toward achieving precision health.” Science translational medicine 10.430 (2018): eaao3612. link
• (Read - no review) Miotto, Riccardo, et al. “Reflecting health: smart mirrors for personalized medicine.” npj Digital Medicine 1.1 (2018): 62. link
• Rudovic, Ognjen, et al. “Personalized machine learning for robot perception of affect and engagement in autism therapy.” Science Robotics 3.19 (2018). link
• Cho, Youngjun, et al. “Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging.” arXiv preprint arXiv:1901.00449 (2018). link
• (optional review) Nakisa, Bahareh, et al. “Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework.” IEEE Access 6 (2018). link
• (Skim) Smets, Elena, et al. “Large-scale wearable data reveal digital phenotypes for daily-life stress detection.” npj Digital Medicine 1.1 (2018): 67. link

Week 10 / Mar 20, 2019 / Spring break

• No class

Week 11 / Mar 27, 2019

• Wu, Nan, et al. “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening.” arXiv preprint arXiv:1903.08297 (2019). link and GitHub
• (skim) Cheng, Jie-Zhi, et al. “Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans.” Scientific reports 6 (2016): 24454. link
• Signaevsky, Maxim, et al. “Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.” Laboratory Investigation (2019): 1. link
• (optional review) Havaei, Mohammad, et al. “Brain tumor segmentation with deep neural networks.” Medical image analysis 35 (2017): 18-31. link
• (optional review) Weston, Alexander D., et al. “Automated abdominal segmentation of CT scans for body composition analysis using deep learning.” Radiology (2018): 181432. link
• (skim) Korfiatis, Panagiotis, et al. “Residual deep convolutional neural network predicts MGMT methylation status.” Journal of digital imaging 30.5 (2017): 622-628. link
• (skim) Cole, James H., et al. “Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.” NeuroImage 163 (2017): 115-124. link
• (skim) Hosseini-Asl, Ehsan, Robert Keynton, and Ayman El-Baz. “Alzheimer’s disease diagnostics by adaptation of 3D convolutional network.” Image Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016. link

Week 12 / Apr 3, 2019

• Rotmensch, Maya, et al. “Learning a health knowledge graph from electronic medical records.” Scientific reports 7.1 (2017): 5994. link
• Gkotsis, George, et al. “Characterisation of mental health conditions in social media using Informed Deep Learning.” Scientific reports 7 (2017): 45141. link
• (optional review) Nie, Liqiang, et al. “Disease inference from health-related questions via sparse deep learning.” IEEE Transactions on knowledge and Data Engineering 27.8 (2015): 2107-2119. link
• (optional review) Corcoran, Cheryl M., et al. “Prediction of psychosis across protocols and risk cohorts using automated language analysis.” World Psychiatry 17.1 (2018): 67-75. link
• (optional review) Fitzpatrick, Kathleen Kara, Alison Darcy, and Molly Vierhile. “Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial.” JMIR mental health 4.2 (2017). link and website
• (skim) Coventry, Peter, et al. “Satisfaction with a digitally-enabled telephone health coaching intervention for people with non-diabetic hyperglycaemia.” npj Digital Medicine 2.1 (2019): 5. link
• (skim) Tsakalidis, Adam, et al. “Can We Assess Mental Health Through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2018. link

Week 13 / Apr 10, 2019

• Liu, Yun, et al. “Detecting cancer metastases on gigapixel pathology images.” arXiv preprint arXiv:1703.02442 (2017). link
• Che, Zhengping, et al. “Recurrent neural networks for multivariate time series with missing values.” Scientific reports 8.1 (2018): 6085. link
• (optional review) Baumel, Tal, et al. “Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment.” arXiv preprint arXiv:1709.09587 (2017). link
• (optional review) Wang, Xiang, et al. “Unsupervised learning of disease progression models.” Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014. link

Week 14 / Apr 17, 2019

• (Read - no review) Finlayson, Samuel G., et al. “Adversarial attacks on medical machine learning.” Science 363.6433 (2019). link
• Mirsky, Yisroel, et al. “CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning.” arXiv preprint arXiv:1901.03597 (2019). link
• Gurovich, Yaron, et al. “Identifying facial phenotypes of genetic disorders using deep learning.” Nature medicine 25.1 (2019): 60. link
• (optional review) Chekroud, Adam Mourad, et al. “Cross-trial prediction of treatment outcome in depression: a machine learning approach.” The Lancet Psychiatry 3.3 (2016). link
• (optional review) Avati, Anand, et al. “Improving palliative care with deep learning.” arXiv preprint arXiv:1711.06402 (2017). link

Week 15 / Apr 24, 2019

• (review from week3) Lippert, Christoph, et al. “Identification of individuals by trait prediction using whole-genome sequencing data.” Proceedings of the National Academy of Sciences 114.38 (2017): 10166-10171. paper and supporting materials and response and response to response.
• Shomorony, Ilan, et al. “Unsupervised integration of multimodal dataset identifies novel signatures of health and disease.” bioRxiv (2018): 432641. link
• (skim) Thompson, Luke R., et al. “A communal catalogue reveals Earth’s multiscale microbial diversity.” Nature 551.7681 (2017). link

Week 16 / May 1, 2019 / Final paper due

• (Poster presentation)

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