Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine. - Lin Lawrence Guo; Stephen R. Pfohl; Jason Alan Fries; José D. Posada; Scott L. Fleming; Catherine Aftandilian; Nigam Shah; Lillian Sung (Appl. Clin. Inform., 2021)

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. - Joshua Lemmon; Lin Lawrence Guo; Ethan Steinberg; Keith E. Morse; Scott Lanyon Fleming; Catherine Aftandilian; Stephen R. Pfohl; José D. Posada; Nigam Shah; Jason Alan Fries; Lillian Sung (J. Am. Medical Informatics Assoc., 2023)

A multi-center study on the adaptability of a shared foundation model for electronic health records. - Lin Lawrence Guo; Jason Alan Fries; Ethan Steinberg; Scott Lanyon Fleming; Keith E. Morse; Catherine Aftandilian; José D. Posada; Nigam Shah; Lillian Sung (npj Digit. Medicine, 2024)

Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare. - Lin Lawrence Guo; Keith E. Morse; Catherine Aftandilian; Ethan Steinberg; Jason Alan Fries; José D. Posada; Scott Lanyon Fleming; Joshua Lemmon; Karim Jessa; Nigam Shah; Lillian Sung (BMC Medical Informatics Decis. Mak., 2024)

A roadmap to implementing machine learning in healthcare: from concept to practice. - Adam Paul Yan; Lin Lawrence Guo; Jiro Inoue; Santiago Eduardo Arciniegas; Emily Vettese; Agata Wolochacz; Nicole Crellin-Parsons; Brandon Purves; Steven Wallace; Azaz Patel; Medhat Roshdi; Karim Jessa; et al. (Frontiers Digit. Health, 2025)