Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.

Journal: Inflammatory bowel diseases
PMID: 38533919

Abstract

BACKGROUND: The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in routine clinical care usually characterize ulcerative colitis disease activity using free text description, limiting their utility for clinical research and quality improvement. We sought to develop algorithms to classify colonoscopy reports according to their MES.

Authors

  • Anna L Silverman
    Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Phoenix, Arizona, USA.
  • Balu Bhasuran
    DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India.
  • Arman Mosenia
    UCSF School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
  • Fatema Yasini
    Department of Computer Science, University of California, Berkeley, Berkeley, CA, USA.
  • Gokul Ramasamy
    Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Saransh Gupta
    Department of Computer Science, University of California, Berkeley, Berkeley, CA, USA.
  • Taline Mardirossian
    Department of Computer Science, University of California, Berkeley, Berkeley, CA, USA.
  • Rohan Narain
    Department of Computer Science, University of California, Berkeley, Berkeley, CA, USA.
  • Justin Sewell
    Division of Gastroenterology, Department of Medicine, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
  • Atul J Butte
    Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA.
  • Vivek A Rudrapatna
    Bakar Computational Health Sciences Institute, San Francisco, California, USA.

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