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CHIL 2020: Toronto, Ontario, Canada
- Marzyeh Ghassemi:
ACM CHIL '20: ACM Conference on Health, Inference, and Learning, Toronto, Ontario, Canada, April 2-4, 2020 [delayed]. ACM 2020, ISBN 978-1-4503-7046-2 - Niranjani Prasad, Barbara E. Engelhardt, Finale Doshi-Velez:
Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning. 1-9 - Zidi Xiu, Chenyang Tao, Ricardo Henao:
Variational learning of individual survival distributions. 10-18 - Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney:
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. 19-29 - Wei Zhang, Zhaobin Kuang, Peggy L. Peissig, David Page:
Adverse drug reaction discovery from electronic health records with deep neural networks. 30-39 - Yubin Park, Joyce C. Ho:
CaliForest: calibrated random forest for health data. 40-50 - Ruru Zhang, Jiawen He, Shenda Shi, Haihong E, Zhonghong Ou, Meina Song:
BMM-Net: automatic segmentation of edema in optical coherence tomography based on boundary detection and multi-scale network. 51-59 - Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin, Ricardo Henao:
Survival cluster analysis. 60-68 - Joseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee:
An adversarial approach for the robust classification of pneumonia from chest radiographs. 69-79 - Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael D. Howell, Greg Corrado, Claire Cui, Moritz Hardt:
Explaining an increase in predicted risk for clinical alerts. 80-89 - Kathleen M. Lewis, Natalia S. Rost, John V. Guttag, Adrian V. Dalca:
Fast learning-based registration of sparse 3D clinical images. 90-98 - Thomas Hooven, Yun Chao Lin, Ansaf Salleb-Aouissi:
Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome data. 99-109 - Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi:
Hurtful words: quantifying biases in clinical contextual word embeddings. 110-120 - Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk:
Disease state prediction from single-cell data using graph attention networks. 121-130 - Shaun Gupta, Frederik Dieleman, Patrick Long, Orla M. Doyle, Nadejda Leavitt:
Using SNOMED to automate clinical concept mapping. 131-138 - Eric Wonhee Lee, Byron C. Wallace, Karla I. Galaviz, Joyce C. Ho:
MMiDaS-AE: multi-modal missing data aware stacked autoencoder for biomedical abstract screening. 139-150 - Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré:
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. 151-159 - Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia:
Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment. 160-169 - Sanja Scepanovic, Enrique Martin-Lopez, Daniele Quercia, Khan Baykaner:
Extracting medical entities from social media. 170-181 - Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara:
Population-aware hierarchical bayesian domain adaptation via multi-component invariant learning. 182-192 - Ardavan Afshar, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter F. Stewart, Joyce C. Ho, Jimeng Sun:
TASTE: temporal and static tensor factorization for phenotyping electronic health records. 193-203 - Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. 204-213 - Alistair E. W. Johnson, Lucas Bulgarelli, Tom J. Pollard:
Deidentification of free-text medical records using pre-trained bidirectional transformers. 214-221 - Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann:
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. 222-235
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