Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. A lung cancer cohort was derived from clinical data from patients attending an Aus...

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Bibliographic Details
Published in:JAMIA open Vol. 3; no. 2; pp. 209 - 215
Main Authors: Alexander, Marliese, Solomon, Benjamin, Ball, David L, Sheerin, Mimi, Dankwa-Mullan, Irene, Preininger, Anita M, Jackson, Gretchen Purcell, Herath, Dishan M
Format: Journal Article
Language:English
Published: United States Oxford University Press 01-07-2020
Subjects:
Artificial intelligence
Cancer
Cancer patients
Clinical trials
Computational linguistics
Diagnosis
Language processing
Lung cancer
Machine learning
Natural language interfaces
Oncology, Experimental
Product development
Research and Applications
Australia
natural language processing
machine learning
clinical trial matching
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Summary:The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.
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ISSN:2574-2531
2574-2531
DOI:10.1093/jamiaopen/ooaa002