A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records. Issue 4 (20th August 2019)

• Record Type: Journal Article
• Authors: Karystianis, George; Florez-Vargas, Oscar; Butler, Tony; Nenadic, Goran
• Is Part Of: JAMIA open. Volume 2:Issue 4(2019)
• Page Start: 521
• Page End: 527
• Publication Date: 2019-08-20
• Subjects: text mining -- clinical trial -- rule-based approach -- dictionaries; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooz041
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: Achieving unbiased recognition of eligible patients for clinical trials from their narrative longitudinal clinical records can be time consuming. We describe and evaluate a knowledge-driven method that identifies whether a patient meets a selected set of 13 eligibility clinical trial criteria from their longitudinal clinical records, which was one of the tasks of the 2018 National NLP Clinical Challenges. Materials and Methods: The approach developed uses rules combined with manually crafted dictionaries that characterize the domain. The rules are based on common syntactical patterns observed in text indicating or describing explicitly a criterion. Certain criteria were classified as "met" only when they occurred within a designated time period prior to the most recent narrative of a patient record and were dealt through their position in text. Results: The system was applied to an evaluation set of 86 unseen clinical records and achieved a microaverage F1-score of 89.1% (with a micro F1-score of 87.0% and 91.2% for the patients that met and did not meet the criteria, respectively). Most criteria returned reliable results (drug abuse, 92.5%; Hba1c, 91.3%) while few (eg, advanced coronary artery disease, 72.0%; myocardial infarction within 6 months of the most recent narrative, 47.5%) proved challenging enough. Conclusion: Overall, the results are encouraging and indicate that automated text mining methods can be used to process clinical records to recognize whether a patient meets a set of clinical trial criteria and could be leveraged to reduce the workload of humans screening patients for trials.