Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.
OBJECTIVE
To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.


METHODS
We annotated 891 interventional cancer trials from ClinicalTrials.gov based on their eligibility for human immunodeficiency virus (HIV)-positive patients using their eligibility criteria. These annotations were used to develop classifiers based on regular expressions and machine learning (ML). After evaluating classification of cancer trials for eligibility of HIV-positive patients, we sought to evaluate the generalizability of our approach to more general diseases and conditions. We annotated the eligibility criteria for 1570 of the most recent interventional trials from ClinicalTrials.gov for HIV-positive and pregnancy eligibility, and the classifiers were retrained and reevaluated using these data.


RESULTS
On the cancer-HIV dataset, the baseline regex model, the bag-of-words ML classifier, and the ML classifier with named entity recognition (NER) achieved macro-averaged F2 scores of 0.77, 0.87, and 0.87, respectively; the addition of NER did not result in a significant performance improvement. On the general dataset, ML + NER achieved macro-averaged F2 scores of 0.91 and 0.85 for HIV and pregnancy, respectively.


CONCLUSIONS
The eligibility status of specific patient populations, such as persons living with HIV and pregnant women, for clinical trials is of interest to both patients and clinicians. We show that it is feasible to develop a high-performing, automated trial classification system for eligibility status that can be integrated into consumer-facing search engines as well as patient-trial matching systems.
College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA.
Kevin
K
Zhang
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Dina
D
Demner-Fushman
D002986:Clinical Trials as Topic / Q000145:classification*; D066264:Datasets as Topic; D004608:Eligibility Determination / Q000379:methods*; D006679:HIV Seropositivity; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D009369:Neoplasms; D018579:Patient Selection
clinical trial screening; eligibility determination; machine learning; natural language processing; patient-trial matching