Natural language inference for curation of structured clinical registries from unstructured text
Journal of the American Medical Informatics Association2021Vol. 29(1), pp. 97–108
Citations Over TimeTop 12% of 2021 papers
Abstract
Traditional natural language processing methods require specially annotated training sets or the construction of a separate model for each registry field. In contrast, a single pretrained NLI model can curate dozens of different fields simultaneously. Surprisingly, NLI methods remain unexplored in the clinical domain outside the realm of shared tasks and benchmarks. Modern NLI models could increase the efficiency of registry curation, even when applied "out of the box" with no additional training.
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