Ideas for how informaticians can get involved with COVID-19 research. - Jason H. Moore; Ian Barnett; Mary Regina Boland; Yong Chen 016; George Demiris; Graciela Gonzalez-Hernandez; Daniel S. Herman; Blanca E. Himes; Rebecca A. Hubbard; Dokyoon Kim; Jeffrey S. Morris; Danielle L. Mowery; et al. (BioData Min., 2020)

A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients. - Lingjiao Zhang; Xiruo Ding; Yanyuan Ma; Naveen Muthu; Imran Ajmal; Jason H. Moore; Daniel S. Herman; Jinbo Chen (J. Am. Medical Informatics Assoc., 2020)

Inference for the Case Probability in High-dimensional Logistic Regression. - Zijian Guo 003; Prabrisha Rakshit; Daniel S. Herman; Jinbo Chen (J. Mach. Learn. Res., 2021)

A flexible symbolic regression method for constructing interpretable clinical prediction models. - William G. La Cava; Paul C. Lee; Imran Ajmal; Xiruo Ding; Priyanka Solanki; Jordana B. Cohen; Jason H. Moore; Daniel S. Herman (npj Digit. Medicine, 2023)

Lessons for local oversight of AI in medicine from the regulation of clinical laboratory testing. - Daniel S. Herman; Jenna T. Reece; Gary E. Weissman (npj Digit. Medicine, 2024)

Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models. - Guilherme Seidyo Imai Aldeia; Daniel S. Herman; William G. La Cava (CoRR, 2025)