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Michael W. Sjoding
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2020 – today
- 2024
- [c8]Sarah Jabbour
, Gregory Kondas, Ella Kazerooni
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. ECCV (64) 2024: 35-51 - [i7]Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens:
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. CoRR abs/2407.14509 (2024) - 2023
- [j7]Negar Farzaneh
Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome. npj Digit. Medicine 6 (2023) - [i6]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR abs/2305.01738 (2023) - 2022
- [j6]Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding:
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. J. Am. Medical Informatics Assoc. 29(6): 1060-1068 (2022) - [c7]Trenton Chang, Michael W. Sjoding, Jenna Wiens:
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. MLHC 2022: 343-390 - [c6]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS 2022 - [c5]Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens:
Learning Concept Credible Models for Mitigating Shortcuts. NeurIPS 2022 - [e2]Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. Proceedings of Machine Learning Research 182, PMLR 2022 [contents] - [i5]Trenton Chang, Michael W. Sjoding, Jenna Wiens:
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. CoRR abs/2208.01127 (2022) - 2021
- [j5]Narathip Reamaroon
Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features. Comput. Biol. Medicine 134: 104463 (2021) - [j4]Elyas Sabeti
Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome. IEEE J. Biomed. Health Informatics 25(3): 784-796 (2021) - [e1]Ken Jung, Serena Yeung, Mark P. Sendak, Michael W. Sjoding, Rajesh Ranganath:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2021, 6-7 August 2021, Virtual Event. Proceedings of Machine Learning Research 149, PMLR 2021 [contents] - [i4]Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding:
Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. CoRR abs/2108.12530 (2021) - 2020
- [j3]Narathip Reamaroon
Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome. BMC Medical Imaging 20(1): 116 (2020) - [j2]Shengpu Tang
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. 27(12): 1921-1934 (2020) - [c4]Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens:
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. ICML 2020: 9387-9396 - [c3]Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens:
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. MLHC 2020: 750-782 - [i3]Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens:
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. CoRR abs/2007.12678 (2020) - [i2]Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens:
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. CoRR abs/2009.10132 (2020)
2010 – 2019
- 2019
- [j1]Narathip Reamaroon
Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome. IEEE J. Biomed. Health Informatics 23(1): 407-415 (2019) - [c2]Elyas Sabeti, Joshua Drews, Narathip Reamaroon, Jonathan Gryak
Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information. EMBC 2019: 1717-1720 - [c1]Jeeheh Oh
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. MLHC 2019: 27-52 - [i1]Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens:
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. CoRR abs/1906.02898 (2019)
Coauthor Index
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last updated on 2024-12-02 21:26 CET by the dblp team
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