Natural language inference for curation of structured clinical registries from unstructured text.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Dec 28, 2021

Abstract

OBJECTIVE: Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. Registry curation, a manual and time-intensive process, is always costly and often impossible for rare or underfunded diseases. Our goal was to evaluate the feasibility of natural language inference (NLI) as a scalable solution for registry curation.

Authors

  • Bethany Percha
    Program in Biomedical Informatics, Stanford University, Stanford, California, USA.
  • Kereeti Pisapati
    Mount Sinai Innovation Partners, Mount Sinai Health System, New York, New York, USA.
  • Cynthia Gao
    Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Hank Schmidt
    Breast Surgical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

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