A semantic framework for intelligent matchmaking for clinical trial eligibility criteria

Abstract

An integral step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified as inclusion and exclusion criteria for each study in freetext form. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably and computationally construed to identify potential subjects. Standardization of the representation of eligibility criteria can enhance the efficiency and accuracy of this process. This article presents a semantic framework that facilitates intelligent matchmaking by identifying a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to existing top-down manual standardization efforts, a bottom-up data driven approach is presented to find a canonical nonredundant representation of an arbitrary collection of clinical trial criteria. The methodology has been validated with a corpus of 709 clinical trials related to Generalized Anxiety Disorder containing 2,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. An eligibility criteria ontology has been constructed based on the clustering. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the effectiveness of the methodology in characterizing clinical trials and subjects and accurate matching between them.