Addressing bias in prediction models by improving subpopulation calibration. - Noam Barda; Gal Yona; Guy N. Rothblum; Philip Greenland; Morton Leibowitz; Ran D. Balicer; Eitan Bachmat; Noa Dagan (J. Am. Medical Informatics Assoc., 2021)

Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs. - Noam Barda; Noa Dagan; Amos Stemmer; Janni Yuval; Eitan Bachmat; Eldad Elnekave; Ran D. Balicer (J. Digit. Imaging, 2022)

Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector. - Unai Fischer Abaigar; Christoph Kern 001; Noam Barda; Frauke Kreuter (CoRR, 2023)

The value of parental medical records for the prediction of diabetes and cardiovascular disease: a novel method for generating and incorporating family histories. - Yuval Barak-Corren; David Tsurel; Daphna Keidar; Ilan Gofer; Dafna Shahaf; Maya Leventer-Roberts; Noam Barda; Ben Y. Reis (J. Am. Medical Informatics Assoc., 2023)

Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector. - Unai Fischer Abaigar; Christoph Kern 001; Noam Barda; Frauke Kreuter (Gov. Inf. Q., 2024)

Multiaccuracy for Subpopulation Calibration Over Distribution Shift in Medical Prediction Models. - Daniel Kapash; Noam Barda; Omer Reingold; Noa Dagan; Ran D. Balicer (CHIL, 2025)