Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

Journal: JCO clinical cancer informatics
PMID: 31977254

Abstract

PURPOSE: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials.

Authors

  • J Thaddeus Beck
    Research Department, Highlands Oncology Group, Fayetteville, AR.
  • Melissa Rammage
    IBM Watson Health, IBM Corporation, Cambridge, MA.
  • Gretchen P Jackson
    IBM Watson Health, IBM Corporation, Cambridge, MA.
  • Anita M Preininger
    IBM Watson Health, IBM Corporation, Cambridge, MA.
  • Irene Dankwa-Mullan
    1 IBM Corporation, Watson Health, Bethesda, Maryland.
  • M Christopher Roebuck
    RxEconomics, Hunt Valley, MD.
  • Adam Torres
    Highlands Oncology Group, Rogers, AR.
  • Helen Holtzen
    Research Department, Highlands Oncology Group, Fayetteville, AR.
  • Sadie E Coverdill
    IBM Watson Health, IBM Corporation, Cambridge, MA.
  • M Paul Williamson
    US Oncology Medical, Novartis Pharmaceuticals Corporation, East Hanover, NJ.
  • Quincy Chau
    US Oncology Medical, Novartis Pharmaceuticals Corporation, East Hanover, NJ.
  • Kyu Rhee
    6 IBM Corporation, Watson Health, Cambridge, Massachusetts.
  • Michael Vinegra
    US Oncology Medical, Novartis Pharmaceuticals Corporation, East Hanover, NJ.

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