Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations. (19th February 2017)

• Record Type: Journal Article
• Authors: Zhang, Kevin; Demner-Fushman, Dina
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 4(2017:Jul.)
• Page Start: 781
• Page End: 787
• Publication Date: 2017-02-19
• Subjects: clinical trial screening -- eligibility determination -- machine learning -- natural language processing -- patient-trial matching; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocw176
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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. Materials and 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. Discussion and Conclusion: 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.