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ORCIDMLHC 2016: Sydney, Australia
- Finale Doshi-Velez, Jim Fackler, David C. Kale, Byron C. Wallace, Jenna Wiens:
Proceedings of the 1st Machine Learning in Health Care, MLHC 2016, Los Angeles, CA, USA, August 19-20, 2016. JMLR Workshop and Conference Proceedings 56, JMLR.org 2016
Accepted Papers
- Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne:
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. 1-16 - Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh:
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. 17-41 - Joseph Futoma, Mark P. Sendak, Blake Cameron, Katherine A. Heller:
Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data. 42-54 - Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Thomas Koch, Alex Rybchuk, Axinia Radeva, Ashwath Rajan, Yiwen Huang, Hatim Diab, Ashish Tomar, Ronald J. Wapner:
Using Kernel Methods and Model Selection for Prediction of Preterm Birth. 55-72 - Narges Razavian, Jake Marcus, David A. Sontag:
Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. 73-100 - Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
Deep Survival Analysis. 101-114 - Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniecky, Orrin Devinsky, Jennifer G. Dy, Carla E. Brodley:
Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy. 115-133 - Rhiannon V. Rose, Daniel J. Lizotte:
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity. 134-149 - Yun Liu
, Collin M. Stultz, John V. Guttag, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su:
Transferring Knowledge from Text to Predict Disease Onset. 150-163 - Truyen Tran, Wei Luo, Dinh Q. Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh:
Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data. 164-177 - Pierre Thodoroff, Joelle Pineau, Andrew Lim:
Learning Robust Features using Deep Learning for Automatic Seizure Detection. 178-190 - Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs:
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations. 191-208 - Yoni Halpern, Steven Horng, David A. Sontag:
Clinical Tagging with Joint Probabilistic Models. 209-225 - Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings with IBTM. 226-238 - Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag:
Uncovering Voice Misuse Using Symbolic Mismatch. 239-252 - Zachary C. Lipton, David C. Kale, Randall C. Wetzel:
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. 253-270 - John A. Quinn, Rose Nakasi, Pius K. B. Mugagga, Patrick Byanyima, William Lubega, Alfred Andama:
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics. 271-281 - Yanbo Xu, Yanxun Xu, Suchi Saria:
A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series. 282-300 - Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun:
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. 301-318
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