Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information. - Elyas Sabeti; Joshua Drews; Narathip Reamaroon; Jonathan Gryak; Michael W. Sjoding; Kayvan Najarian (EMBC, 2019)

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. - Jeeheh Oh; Jiaxuan Wang; Shengpu Tang; Michael W. Sjoding; Jenna Wiens (MLHC, 2019)

Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome. - Narathip Reamaroon; Michael W. Sjoding; Kaiwen Lin; Theodore J. Iwashyna; Kayvan Najarian (IEEE J. Biomed. Health Informatics, 2019)

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. - Shengpu Tang; Aditya Modi 002; Michael W. Sjoding; Jenna Wiens (ICML, 2020)

Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. - Sarah Jabbour; David Fouhey; Ella Kazerooni; Michael W. Sjoding; Jenna Wiens (MLHC, 2020)

Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome. - Narathip Reamaroon; Michael W. Sjoding; Harm Derksen; Elyas Sabeti; Jonathan Gryak; Ryan P. Barbaro; Brian D. Athey; Kayvan Najarian (BMC Medical Imaging, 2020)

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. - Shengpu Tang; Parmida Davarmanesh; Yanmeng Song; Danai Koutra; Michael W. Sjoding; Jenna Wiens (J. Am. Medical Informatics Assoc., 2020)

Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features. - Narathip Reamaroon; Michael W. Sjoding; Jonathan Gryak; Brian D. Athey; Kayvan Najarian; Harm Derksen (Comput. Biol. Medicine, 2021)

Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. - Sarah Jabbour; David Fouhey; Ella Kazerooni; Jenna Wiens; Michael W. Sjoding (CoRR, 2021)

Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome. - Elyas Sabeti; Joshua Drews; Narathip Reamaroon; Elisa Warner; Michael W. Sjoding; Jonathan Gryak; Kayvan Najarian (IEEE J. Biomed. Health Informatics, 2021)

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. - Trenton Chang; Michael W. Sjoding; Jenna Wiens (MLHC, 2022)

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. - Shengpu Tang; Maggie Makar; Michael W. Sjoding; Finale Doshi-Velez; Jenna Wiens (NeurIPS, 2022)

Learning Concept Credible Models for Mitigating Shortcuts. - Jiaxuan Wang; Sarah Jabbour; Maggie Makar; Michael W. Sjoding; Jenna Wiens (NeurIPS, 2022)

Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. - Sarah Jabbour; David Fouhey; Ella Kazerooni; Jenna Wiens; Michael W. Sjoding (J. Am. Medical Informatics Assoc., 2022)

Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome. - Negar Farzaneh; Sardar Ansari; Elizabeth Lee; Kevin R. Ward; Michael W. Sjoding (npj Digit. Medicine, 2023)

DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. - Sarah Jabbour; Gregory Kondas; Ella Kazerooni; Michael W. Sjoding; David Fouhey; Jenna Wiens (ECCV (64), 2024)

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making. - Sarah Jabbour; David Fouhey; Nikola Banovic 001; Stephanie D. Shepard; Ella Kazerooni; Michael W. Sjoding; Jenna Wiens (CoRR, 2025)