Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports.

Journal: Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
PMID: 39197747

Abstract

BACKGROUND: We aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports.

Authors

  • Ricardo Loor-Torres
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Yuqi Wu
    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
  • Esteban Cabezas
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Mariana Borras-Osorio
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • David Toro-Tobon
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota; Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic, Rochester, Minnesota.
  • Mayra Duran
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Misk Al Zahidy
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Maria Mateo Chavez
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Cristian Soto Jacome
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Jungwei W Fan
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
  • Naykky M Singh Ospina
    Division of Endocrinology, Department of Medicine, University of Florida, Gainesville, Florida.
  • Yonghui Wu
    Department of Health Outcomes and Biomedical Informatics.
  • Juan P Brito
    Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota; Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic, Rochester, Minnesota. Electronic address: brito.juan@mayo.edu.

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