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Cited by 127Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsMachine Learning in Healthcare

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

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

Beck, J. Thaddeus, Rammage, Melissa, Jackson, Gretchen P., Preininger, Anita M., Dankwa-Mullan, Irene, Roebuck, M. Christopher, Torres, Adam, Holtzen, Helen, Coverdill, Sadie E., Williamson, M. Paul, Chau, Quincy, Rhee, Kyu, Vinegra, Michael·2020·JCO Clinical Cancer Informatics
DOI 10.1200/cci.19.00079
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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. PATIENTS AND METHODS: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS: Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION: In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

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Crossref · 10.1200/cci.19.00079

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  1. Shivade C, 2017, AMIA Annu Symp Proc, 2016, 1149

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Metadata

Authors
Beck, J. T., Rammage, M., Jackson, G. P., Preininger, A. M., Dankwa-Mullan, I., Roebuck, M. C., Torres, A., Holtzen, H., Coverdill, S. E., Williamson, M. P., Chau, Q., Rhee, K., & Vinegra, M.
Affiliations
  • Highlands Oncology Group (US)
  • IBM (United States) (US)
  • IBM Watson Health, IBM Corporation, Cambridge, MA
  • RxEconomics, Hunt Valley, MD
  • Novartis (United States) (US)
ORCID
Venue
JCO Clinical Cancer Informatics
Publisher
American Society of Clinical Oncology (ASCO)
Published
2020-11
Volume
4
Issue
4
Pages
50-59
ISSN
2473-4276 (ISSN-L)
DOI
10.1200/cci.19.00079
OpenAlex
W3001688612
Cited by
127
References
19
Citations by year
2026: 17 · 2025: 28 · 2024: 33 · 2023: 20 · 2022: 7 · 2021: 16 · 2020: 5
FWCI
3.58
Type
journal article
Language
en
Open access
closed
Locations
  • JCO Clinical Cancer Informatics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
clinical trial eligibility screening, natural language processing, machine learning, breast cancer, cancer clinical trials, trial accrual, attribute extraction
MeSH terms
Machine Learning, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Breast Neoplasms, Clinical Trials as Topic, Eligibility Determination, Female, Humans, Middle Aged, Natural Language Processing, Patient Selection, Community Networks, Early Detection of Cancer

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