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MLHC 2019: Ann Arbor, Michigan, USA
- Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. Proceedings of Machine Learning Research 106, PMLR 2019 - Michael Moor, Max Horn
, Bastian Rieck
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. 2-26 - Jeeheh Oh
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. 27-52 - Surya Teja Devarakonda, Yeahuay Joie Wu, Yi Ren Fung, Madalina Fiterau:
FLARe: Forecasting by Learning Anticipated Representations. 53-65 - Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noémie Elhadad:
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. 66-90 - Mohammad Akbari, Rumi Chunara:
Using Contextual Information to Improve Blood Glucose Prediction. 91-108 - Chirag Nagpal, Xinyu Li, Michael R. Pinsky, Artur Dubrawski:
Dynamically Personalized Detection of Hemorrhage. 109-123 - Divya Shanmugam, Davis W. Blalock, John V. Guttag:
Multiple Instance Learning for ECG Risk Stratification. 124-139 - Supriya Nagesh, Alexander Moreno, Hiroshi Ishikawa, Gadi Wollstein, Joel S. Schuman, James M. Rehg:
A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM. 140-159 - Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po:
Temporal Graph Convolutional Networks for Automatic Seizure Detection. 160-180 - Ognjen (Oggi) Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard:
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes. 181-196 - Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing:
Multimodal Machine Learning for Automated ICD Coding. 197-215 - Bhanu Pratap Singh Rawat, Fei Li, Hong Yu:
Clinical Judgement Study using Question Answering from Electronic Health Records. 216-229 - Bonggun Shin, Sungsoo Park, Keunsoo Kang, Joyce C. Ho:
Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. 230-248 - Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi:
Clinically Accurate Chest X-Ray Report Generation. 249-269 - Weirui Kong, Hyeju Jang, Giuseppe Carenini, Thalia Shoshana Field
A Neural Model for Predicting Dementia from Language. 270-286 - Grace Guan, Barbara E. Engelhardt:
Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis. 271-287 - Youran Qi, Qi Tang:
Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning. 288-303 - Victor Alfonso Rodriguez, Adler J. Perotte:
Phenotype Inference with Semi-Supervised Mixed Membership Models. 304-324 - Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah:
Counterfactual Reasoning for Fair Clinical Risk Prediction. 325-358 - Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. 359-380 - Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. 381-405 - Avijit Thawani, Michael J. Paul, Urmimala Sarkar, Byron C. Wallace:
Are Online Reviews of Physicians Biased Against Female Providers? 406-423 - Steve Yadlowsky, Sanjay Basu, Lu Tian:
A Calibration Metric for Risk Scores with Survival Data. 424-450 - Jinsung Yoon, James Jordon, Mihaela van der Schaar:
ASAC: Active Sensing using Actor-Critic models. 451-473 - Min Zheng, Samantha Kleinberg:
Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series. 474-489 - Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak:
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. 490-512 - Siddharth Biswal, Cao Xiao, M. Brandon Westover, Jimeng Sun:
EEGtoText: Learning to Write Medical Reports from EEG Recordings. 513-531 - Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chaplain, David A. Sontag, Xavier Amatriain:
Few-Shot Learning for Dermatological Disease Diagnosis. 532-552 - David Dov, Shahar Z. Kovalsky, Jonathan Cohen, Danielle Elliott Range, Ricardo Henao, Lawrence Carin:
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images. 553-570 - Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar:
Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis. 571-591 - Hao-Chih Lee, Sarah T. Cherng, Riccardo Miotto, Joel T. Dudley:
Enhancing high-content imaging for studying microtubule networks at large-scale. 592-613 - Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang:
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. 614-640 - Mark Mirtchouk, Dana L. McGuire, Andrea L. Deierlein, Samantha Kleinberg:
Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments. 641-662 - Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian J. McAuley, Zachary C. Lipton:
Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions. 663-679 - Shiva Kaul, Anthony Falco, Karianne Anthes:
Measuring the Sympathetic Response to Intense Exercise in a Practical Setting. 680-703 - Jose Javier Gonzalez Ortiz, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag, Marzyeh Ghassemi:
Learning from Few Subjects with Large Amounts of Voice Monitoring Data. 704-720 - Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun:
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules. i
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