Abstract: Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC). Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no rec...
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Topics:
Artificial intelligence
Natural language processing
Internal medicine