Semi-Automatically Inducing Semantic Classes of Clinical Research Eligibility Criteria Using UMLS and Hierarchical Clustering.

This paper presents a novel approach to learning semantic classes of clinical research eligibility criteria. It uses the UMLS Semantic Types to represent semantic features and the Hierarchical Clustering method to group similar eligibility criteria. By establishing a gold standard using two independent raters, we evaluated the coverage and accuracy of the induced semantic classes. On 2,718 random eligibility criteria sentences, the inter-rater classification agreement was 85.73%. In a 10-fold validation test, the average Precision, Recall and F-score of the classification results of a decision-tree classifier were 87.8%, 88.0%, and 87.7% respectively. Our induced classes well aligned with 16 out of 17 eligibility criteria classes defined by the BRIDGE model. We discuss the potential of this method and our future work.