A natural language processing (NLP) algorithm was successfully developed and validated to accurately identify esophageal and gastric precancerous conditions and cancers from unstructured electronic health records (EHR). According to a study published in Gastro Hep Advances last month, researchers relied on the VA Million Veteran Program (VA MVP), where their rule-based algorithm achieved excellent accuracy (ranging from 97.5% to 100%) in identifying conditions such as Barrett’s esophagus (BE) and gastric intestinal metaplasia (GIM) from pathology reports.

Gastric and esophageal cancers remain among the leading causes of cancer mortality worldwide. Both gastric adenocarcinoma and esophageal adenocarcinoma are typically preceded by identifiable precancerous conditions, GIM and BE, that

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