Expert-Augmented Machine Learning. - Efstathios D. Gennatas; Jerome H. Friedman; Lyle H. Ungar; Romain Pirracchio; Eric Eaton; L. Reichman; Yannet Interian; Charles B. Simone II; A. Auerbach; E. Delgado; Mark J. van der Laan; Timothy D. Solberg; et al. (CoRR, 2019)

The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients. - Ményssa Cherifa; Yannet Interian; Alice Blet; Matthieu Resche-Rigon; Romain Pirracchio (Artif. Intell. Medicine, 2021)

Personalized Online Machine Learning. - Ivana Malenica; Rachael V. Phillips; Romain Pirracchio; Antoine Chambaz; Alan E. Hubbard; Mark J. van der Laan (CoRR, 2021)

Sequential algorithmic modification with test data reuse. - Jean Feng; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio; Alexej Gossmann (UAI, 2022)

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (CoRR, 2022)

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. - Jean Feng; Alexej Gossmann; Berkman Sahiner; Romain Pirracchio (J. Am. Medical Informatics Assoc., 2022)

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. - Jean Feng; Rachael V. Phillips; Ivana Malenica; Andrew Bishara; Alan E. Hubbard; Leo A. Celi; Romain Pirracchio (npj Digit. Medicine, 2022)

Multi-task Highly Adaptive Lasso. - Ivana Malenica; Rachael V. Phillips; Daniel Lazzareschi; Jeremy R. Coyle; Romain Pirracchio; Mark J. van der Laan (CoRR, 2023)

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CoRR, 2023)

A Brief Tutorial on Sample Size Calculations for Fairness Audits. - Harvineet Singh; Fan Xia; Mi-Ok Kim; Romain Pirracchio; Rumi Chunara; Jean Feng (CoRR, 2023)

Is this model reliable for everyone? Testing for strong calibration. - Jean Feng; Alexej Gossmann; Romain Pirracchio; Nicholas Petrick; Gene Pennello; Berkman Sahiner (AISTATS, 2024)

Monitoring machine learning-based risk prediction algorithms in the presence of performativity. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (AISTATS, 2024)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Anthony Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CLeaR, 2024)

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit. - Shiker S. Nair; Alina Guo; Joseph Boen; Ataes Aggarwal; Ojas Chahal; Arushi Tandon; Meer Patel; Sreenidhi Sankararaman; Nicholas J. Durr; Tej D. Azad; Romain Pirracchio; Robert D. Stevens 002 (Comput. Biol. Medicine, 2024)

Challenges with reinforcement learning model transportability for sepsis treatment in emergency care. - Peter C. Nauka; Jason N. Kennedy; Emily B. Brant; Matthieu Komorowski; Romain Pirracchio; Derek C. Angus; Christopher W. Seymour (npj Digit. Medicine, 2025)

Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables. - Junming Shi; Alan E. Hubbard; Nicholas Fong; Romain Pirracchio (BMC Medical Informatics Decis. Mak., 2025)