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2020 – today
- 2023
[j2]Ravi B. Parikh, Yichen Zhang, Likhitha Kolla, Corey Chivers
, Katherine R. Courtright, Jingsan Zhu, Amol S. Navathe, Jinbo Chen:
Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic. J. Am. Medical Informatics Assoc. 30(2): 348-354 (2023)- 2022
- [c6]Sameh N. Saleh, Corey Chivers, Jason Lubken, Katherine R. Courtright, Gary E. Weissman, Daniel Herman:
Monitoring Performance of a Deployed Machine Learning Model that Predicts Long-term Mortality for Targeting Inpatient Palliative Care. AMIA 2022 - [c5]Sean Grullon, Vaughn Spurrier, Jiayi Zhao, Corey Chivers, Yang Jiang, Kiran Motaparthi, Jason B. Lee, Michael J. Bonham, Julianna D. Ianni:
Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression. ECCV Workshops (7) 2022: 442-456 - [i4]Sean Grullon, Vaughn Spurrier, Jiayi Zhao, Corey Chivers, Yang Jiang, Kiran Motaparthi, Michael J. Bonham, Julianna D. Ianni:
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression. CoRR abs/2210.04803 (2022) - 2020
- [j1]Li-Fang Cheng, Bianca Dumitrascu, Gregory Darnell, Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt:
Sparse multi-output Gaussian processes for online medical time series prediction. BMC Medical Informatics Decis. Mak. 20(1): 152 (2020) - [c4]Li-Fang Cheng, Bianca Dumitrascu, Michael Minyi Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt:
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes. AISTATS 2020: 4045-4055
2010 – 2019
- 2019
- [c3]Yindalon Aphinyanaphongs, Jonathan K. Wilt, Corey J. Chivers, Mark P. Sendak:
Translating, Implementing, Deploying, and Evaluating Clinical Interventions Using Machine Learning Based Predictive Models: Illustrative Case Studies. AMIA 2019 - [c2]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Corey Chivers, Andrew Beam, Tristan Naumann, Brett K. Beaulieu-Jones:
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 2019: 1-9 - [i3]Li-Fang Cheng, Bianca Dumitrascu, Michael Minyi Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt:
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes. CoRR abs/1906.00226 (2019) - 2018
- [i2]Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian V. Dalca, Samuel G. Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi:
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR abs/1811.07216 (2018) - 2017
- [c1]Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt:
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units. UAI 2017 - [i1]Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt:
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units. CoRR abs/1704.06300 (2017)
Coauthor Index
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