Clinical decision support for therapeutic decision-making in cancer: A systematic review. - Melissa Beauchemin; Meghan T. Murray; Lillian Sung; Dawn L. Hershman; Chunhua Weng; Rebecca Schnall (Int. J. Medical Informatics, 2019)

Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application. - Sadie Cook; Emily Vettese; Dilip Soman; Shannon Hyslop; Susan Kuczynski; Brenda Spiegler; Hailey Davis; Nathan Duong; Stacee Ou Wai; Robert Golabek; Patryk Golabek; Adam Antoszek-Rallo; et al. (BMC Medical Informatics Decis. Mak., 2019)

A Retrospective Analysis of Machine Learning Driven Antibiotic Selection in the Emergency Department. - Conor K. Corbin; Arhana Chattopadhyah; Lillian Sung; Amy Chang; Stan Deresinski; Jonathan H. Chen (AMIA, 2021)

Data Quality of Chemotherapy-Induced Nausea and Vomiting Documentation. - Melissa Beauchemin; Chunhua Weng; Lillian Sung; Adrienne Pichon; Maura Abbott; Dawn L. Hershman; Rebecca Schnall (Appl. Clin. Inform., 2021)

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)

Integrating Flowsheet Data in OMOP Common Data Model for Clinical Research. - Tina Seto; Lillian Sung; José D. Posada; Priyamvada Desai; Susan Weber; Somalee Datta (CoRR, 2021)

Real-World Data at the Point-of-Care: Exploring Education Needs Across All Stakeholders. - Julian Genkins; Dev Dash; Lillian Sung; Saurabh Gombar; Alison Callahan (AMIA, 2022)

Machine learning approaches to investigate Clostridioides difficile infection and outcomes: A systematic review. - Yujie Chen; Min Xi; Alistair E. W. Johnson; George Tomlinson; Aaron Campigotto; Lingxiao Chen; Lillian Sung (Int. J. Medical Informatics, 2022)

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)

Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions. - Lillian Sung; Michael Brudno; Michael C. W. Caesar; Amol A. Verma; Brad Buchsbaum; Ravi Retnakaran; Vasily Giannakeas; Azadeh Kushki; Gary D. Bader; Helen Lasthiotakis; Muhammad Mamdani; Lisa Strug (Frontiers Digit. Health, 2025)

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)

Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness. - Stephen R. Pfohl; Natalie Harris; Chirag Nagpal; David Madras; Vishwali Mhasawade; Olawale Salaudeen; Awa Dieng; Shannon Sequeira; Santiago Eduardo Arciniegas; Lillian Sung; Nnamdi Ezeanochie; Heather Cole-Lewis; et al. (CoRR, 2025)