Parsable Clinical Trial Eligibility Criteria Representation Using Natural Language Processing

Successful clinical trials offer better treatments to current or future patients and advance scientific research.^1,2,3 Clinical trials define the target population using specific eligibility criteria to ensure an optimal enrollment sample.⁴ Clinical trial eligibility criteria are often described in unstructured free-text⁵ which makes automation of the recruitment process challenging. This contributes to the long-standing problem of insufficient enrollment of clinical trials.^6,7 This study uses a machine learning approach to extract clinical trial eligibility criteria, and convert them into structured queryable formats using descriptive statistics based on medical entity frequency and binary entity relationships. We present a JSON-based structural representation of clinical trials eligibility criteria for clinical trials to follow.