Michael W. Sjoding
Orcid: 0000-0002-0535-9659
According to our database1,
Michael W. Sjoding
authored at least 16 papers
between 2019 and 2024.
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Bibliography
2024
Proceedings of the Computer Vision - ECCV 2024, 2024
2023
Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.
npj Digit. Medicine, 2023
2022
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure.
J. Am. Medical Informatics Assoc., 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.
Proceedings of the Machine Learning for Healthcare Conference, 2022
2021
Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome.
IEEE J. Biomed. Health Informatics, 2021
Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure.
CoRR, 2021
Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features.
Comput. Biol. Medicine, 2021
2020
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.
J. Am. Medical Informatics Assoc., 2020
Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome.
BMC Medical Imaging, 2020
Proceedings of the Machine Learning for Healthcare Conference, 2020
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies.
Proceedings of the 37th International Conference on Machine Learning, 2020
2019
Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.
IEEE J. Biomed. Health Informatics, 2019
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series.
Proceedings of the Machine Learning for Healthcare Conference, 2019
Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019