Dynamic categorization of clinical research eligibility criteria by hierarchical clustering

OBJECTIVE To semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity. DESIGN The UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hierarchical clustering and to train supervised classifiers. MEASUREMENTS We induced 27 categories and measured the prevalence of the categories in 27,278 eligibility criteria from 1578 clinical trials and compared the classification performance (i.e., precision, recall, and F1-score) between the UMLS-based feature representation and the "bag of words" feature representation among five common classifiers in Weka, including J48, Bayesian Network, Naïve Bayesian, Nearest Neighbor, and instance-based learning classifier. RESULTS The UMLS semantic feature representation outperforms the "bag of words" feature representation in 89% of the criteria categories. Using the semantically induced categories, machine-learning classifiers required only 2000 instances to stabilize classification performance. The J48 classifier yielded the best F1-score and the Bayesian Network classifier achieved the best learning efficiency. CONCLUSION The UMLS is an effective knowledge source and can enable an efficient feature representation for semi-automated semantic category induction and automatic categorization for clinical research eligibility criteria and possibly other clinical text.