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ORCIDCHIL 2021: Virtual Event, USA
- Marzyeh Ghassemi, Tristan Naumann, Emma Pierson:
ACM CHIL '21: ACM Conference on Health, Inference, and Learning, Virtual Event, USA, April 8-9, 2021. ACM 2021, ISBN 978-1-4503-8359-2 - Weicheng Zhu, Narges Razavian:
Variationally regularized graph-based representation learning for electronic health records. 1-13 - David Dov, Serge Assaad, Shijing Si, Rui Wang, Hongteng Xu, Shahar Ziv Kovalsky
, Jonathan Bell, Danielle Elliott Range, Jonathan Cohen, Ricardo Henao, Lawrence Carin:
Affinitention nets: kernel perspective on attention architectures for set classification with applications to medical text and images. 14-24 - Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès:
Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction. 25-35 - Diana Mincu, Eric Loreaux, Shaobo Hou, Sebastien Baur, Ivan Protsyuk, Martin Seneviratne, Anne Mottram, Nenad Tomasev, Alan Karthikesalingam, Jessica Schrouff:
Concept-based model explanations for electronic health records. 36-46 - Konstantin D. Pandl, Fabian Feiland, Scott Thiebes, Ali Sunyaev:
Trustworthy machine learning for health care: scalable data valuation with the shapley value. 47-57 - Emma Rocheteau, Pietro Liò, Stephanie L. Hyland:
Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit. 58-68 - Dimitris Spathis, Ignacio Perez-Pozuelo, Søren Brage, Nicholas J. Wareham, Cecilia Mascolo:
Self-supervised transfer learning of physiological representations from free-living wearable data. 69-78 - Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit:
Generative ODE modeling with known unknowns. 79-94 - Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz:
Learning to predict with supporting evidence: applications to clinical risk prediction. 95-104 - Saahil Jain, Akshay Smit, Steven Q. H. Truong, Chanh D. T. Nguyen, Minh-Thanh Huynh, Mudit Jain, Victoria A. Young, Andrew Y. Ng, Matthew P. Lungren, Pranav Rajpurkar:
VisualCheXbert: addressing the discrepancy between radiology report labels and image labels. 105-115 - Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y. Ng, Pranav Rajpurkar:
CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation. 116-124 - Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren:
CheXternal: generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings. 125-132 - Paidamoyo Chapfuwa, Serge Assaad, Shuxi Zeng, Michael J. Pencina, Lawrence Carin, Ricardo Henao:
Enabling counterfactual survival analysis with balanced representations. 133-145 - Bonggun Shin, Sungsoo Park, JinYeong Bak, Joyce C. Ho:
Controlled molecule generator for optimizing multiple chemical properties. 146-153 - Xin Liu, Ziheng Jiang, Josh Fromm, Xuhai Xu, Shwetak N. Patel, Daniel McDuff:
MetaPhys: few-shot adaptation for non-contact physiological measurement. 154-163 - Jean Feng:
Learning to safely approve updates to machine learning algorithms. 164-173 - Saeed Khorram, Tyler Lawson, Fuxin Li:
iGOS++: integrated gradient optimized saliency by bilateral perturbations. 174-182 - Mohamed F. Ghalwash, Zijun Yao, Prithwish Chakraborty, James V. Codella, Daby Sow:
Phenotypical ontology driven framework for multi-task learning. 183-192 - Alvin Chan, Anna Korsakova, Yew-Soon Ong, Fernaldo Richtia Winnerdy, Kah Wai Lim, Anh Tuan Phan:
RNA alternative splicing prediction with discrete compositional energy network. 193-203 - Danliang Ho, Iain Bee Huat Tan, Mehul Motani:
Predictive models for colorectal cancer recurrence using multi-modal healthcare data. 204-213 - Shreshth Saini, Young Seok Jeon, Mengling Feng:
B-SegNet: branched-SegMentor network for skin lesion segmentation. 214-221 - Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger:
Modeling longitudinal dynamics of comorbidities. 222-235 - Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin:
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. 236-245 - Matthew Saponaro, Ajith Vemuri, Greg Dominick, Keith Decker:
Contextualization and individualization for just-in-time adaptive interventions to reduce sedentary behavior. 246-256 - Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A comprehensive EHR timeseries pre-training benchmark. 257-278 - Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi:
An empirical framework for domain generalization in clinical settings. 279-290 - Guimin Dong, Lihua Cai, Debajyoti Datta, Shashwat Kumar, Laura E. Barnes, Mehdi Boukhechba:
Influenza-like symptom recognition using mobile sensing and graph neural networks. 291-300
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