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ORCIDMLHC 2017: Boston, Massachusetts, USA
- Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017. Proceedings of Machine Learning Research 68, PMLR 2017 - Jeremy C. Weiss:
Piecewise-constant parametric approximations for survival learning. 1-12 - Amirreza Farnoosh, Sarah Ostadabbas, Mehrdad Nourani:
Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing. 13-24 - Savannah L. Bergquist, Gabriel A. Brooks, Nancy L. Keating, Mary Beth Landrum, Sherri Rose:
Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data. 25-38 - Jose Castela Forte, Marco A. Wiering, Hjalmar R. Bouma, Fred Geus, Anne H. Epema:
Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models. 39-58 - Madalina Fiterau, Suvrat Bhooshan, Jason A. Fries, Charles Bournhonesque, Jennifer L. Hicks, Eni Halilaj, Christopher Ré, Scott L. Delp:
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information. 59-74 - Albert Haque, Michelle Guo, Alexandre Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Jeffrey Jopling, N. Lance Downing, William Beninati, Amit Singh, Terry Platchek, Arnold Milstein, Li Fei-Fei:
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance. 75-87 - Hei Law, Khurshid Ghani, Jia Deng:
Surgeon Technical Skill Assessment using Computer Vision based Analysis. 88-99 - Nathan H. Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. 100-111 - Samuele Fiorini, Alessandro Verri, Annalisa Barla, Andrea Tacchino, Giampaolo Brichetto:
Temporal prediction of multiple sclerosis evolution from patient-centered outcomes. 112-125 - Matteo Ruffini, Ricard Gavaldà, Esther Limon:
Clustering Patients with Tensor Decomposition. 126-146 - Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach. 147-163 - Yinchong Yang, Peter A. Fasching, Volker Tresp:
Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models. 164-176 - Yujia Bao, Zhaobin Kuang, Peggy L. Peissig, David Page, Rebecca Willett:
Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. 177-190 - Bryan Conroy, Minnan Xu-Wilson, Asif Rahman:
Patient Similarity Using Population Statistics and Multiple Kernel Learning. 191-203 - Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy D. Schmahmann, Frédo Durand, John V. Guttag:
A Video-Based Method for Automatically Rating Ataxia. 204-216 - Maria Jahja, Daniel J. Lizotte:
Visualizing Clinical Significance with Prediction and Tolerance Regions. 217-230 - Elizabeth C. Lorenzi, Stephanie L. Brown, Zhifei Sun, Katherine A. Heller:
Predictive Hierarchical Clustering: Learning clusters of CPT codes for improving surgical outcomes. 231-242 - Joseph Futoma, Sanjay Hariharan, Katherine A. Heller, Mark P. Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien:
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. 243-254 - Kazi T. Islam, Christian R. Shelton, Juan I. Casse, Randall C. Wetzel:
Marked Point Process for Severity of Illness Assessment. 255-270 - Yuan Ling, Sadid A. Hasan, Vivek V. Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri:
Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study. 271-285 - Edward Choi, Siddharth Biswal, Bradley A. Malin, Jon Duke, Walter F. Stewart, Jimeng Sun:
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks. 286-305 - Silvio Amir, Glen Coppersmith, Paula Carvalho, Mário J. Silva, Byron C. Wallace:
Quantifying Mental Health from Social Media with Neural User Embeddings. 306-321 - Harini Suresh, Nathan Hunt, Alistair E. W. Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Clinical Intervention Prediction and Understanding with Deep Neural Networks. 322-337 - Arya A. Pourzanjani, Tie Bo Wu, Richard M. Jiang, Mitchell J. Cohen, Linda R. Petzold:
Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach. 338-351 - Zihan Wang, Michael Brudno, Orion J. Buske:
Towards a Directory of Rare Disease Specialists: Identifying Experts from Publication History. 352-360 - Alistair E. W. Johnson, Tom J. Pollard
, Roger G. Mark:
Reproducibility in critical care: a mortality prediction case study. 361-376
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