Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision. - Sam Preston; Mu Wei; Rajesh Rao; Robert Tinn; Naoto Usuyama; Michael Lucas; Roshanthi Weerasinghe; Soohee Lee; Brian Piening; Paul Tittel; Naveen Valluri; Tristan Naumann; et al. (CoRR, 2022)

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. - Cliff Wong; Sheng Zhang 012; Yu Gu 017; Christine Moung; Jacob Abel; Naoto Usuyama; Roshanthi Weerasinghe; Brian Piening; Tristan Naumann; Carlo Bifulco; Hoifung Poon (MLHC, 2023)

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models. - Javier González Hernández; Cliff Wong; Zelalem Gero; Jass Bagga; Risa Ueno; Isabel Chien; Eduard Oravkin; Emre Kiciman; Aditya V. Nori; Roshanthi Weerasinghe; Rom S. Leidner; Brian Piening; et al. (CoRR, 2023)

Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision. - Sam Preston; Mu Wei; Rajesh Rao; Robert Tinn; Naoto Usuyama; Michael Lucas; Yu Gu 017; Roshanthi Weerasinghe; Soohee Lee; Brian Piening; Paul Tittel; Naveen Valluri; et al. (Patterns, 2023)

Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects. - Isabel Chien; Cliff Wong; Zelalem Gero; Jaspreet Bagga; Risa Ueno; Richard E. Turner; Roshanthi Weerasinghe; Brian Piening; Tristan Naumann; Carlo Bifulco; Hoifung Poon; Javier González Hernández (MLHC, 2024)

Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale. - Cliff Wong; Sam Preston; Qianchu Liu; Zelalem Gero; Jass Bagga; Sheng Zhang 012; Shrey Jain; Theodore Zhao; Yu Gu 017; Yanbo Xu; Sid Kiblawi; Roshanthi Weerasinghe; et al. (CoRR, 2025)