Text Classification-Based Automatic Recruitment of Patients for Clinical Trials: A Silver Standards-Based Case Study

A lack of recruitment of appropriate subjects plagues most clinical research trials. One barrier is an efficient way to identify eligible subjects. Researchers worked to harness computing power to improve automated identification of potential subjects for clinical trials with modest success. We use text classification to automatically identify patients for a hypothetical Acute Coronary Syndrome clinical research study from intensive care unit discharge summaries. We apply several state of the art classification methods including Bayesian Logistic Regression, AdaBoost, Support Vector Machines, and Random Forests to build models from administrative manually assigned ICD-9 codes. We then apply these models to discharge summaries labeled by a board certified cardiologist for patients eligible for the hypothetical research study. The best models perform with 0.95 area under the ROC curve for identifying eligible patients. This pilot study suggests that text-based classification holds promise for identification of potential clinical trial subjects. Our methods require further validation in studies involving multiple inclusion and exclusion criteria.