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20222025
most citedTowards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision

3 citations · 3 across the 1 of their papers we have counts for

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cs.CL2025

Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale

Cliff Wong, Sam Preston, Qianchu Liu +22

A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text…

cs.CL2022★ 3 cited

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 +11

Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and ti…