Clustering clinical trials with similar eligibility criteria features

Abstract

Objectives To automatically identify and cluster clinical trials with similar eligibility features. Methods Using the public repository ClinicalTrials.gov as the data source, we extracted semantic features from the eligibility criteria text of all clinical trials and constructed a trial-feature matrix. We calculated the pairwise similarities for all clinical trials based on their eligibility features. For all trials, by selecting one trial as the center each time, we identified trials whose similarities to the central trial were greater than or equal to a predefined threshold and constructed center-based clusters. Then we identified unique trial sets with distinctive trial membership compositions from center-based clusters by disregarding their structural information. Results From the 145,745 clinical trials on ClinicalTrials.gov, we extracted 5,508,491 semantic features. Of these, 459,936 were unique and 160,951 were shared by at least one pair of trials. Crowdsourcing the cluster evaluation using Amazon Mechanical Turk (MTurk), we identified the optimal similarity threshold, 0.9. Using this threshold, we generated 8,806 center-based clusters. Evaluation of a sample of the clusters by MTurk resulted in a mean score 4.331±0.796 on a scale of 1–5 (5 indicating “strongly agree that the trials in the cluster are similar”). Conclusions We contribute an automated approach to clustering clinical trials with similar eligibility features. This approach can be potentially useful for investigating knowledge reuse patterns in clinical trial eligibility criteria designs and for improving clinical trial recruitment. We also contribute an effective crowdsourcing method for evaluating informatics interventions.

Aggregator: A machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial

“…This is a well-recognized problem that is generally handled by time-consuming manual effort [5, 6]. Document clustering is a mature field, and several other studies have clustered PubMed articles according to, e.g., textual and semantic similarity [17–20] or clustered clinicaltrials.gov registry entries by shared clinical trial eligibility criteria [21]. However, to our knowledge, ours is the first study that has investigated the ability of MEDLINE and PubMed metadata to identify articles that derive from the same underlying trial.…”

Visual aggregate analysis of eligibility features of clinical trials

“…One of our previously published papers also discovered that many clinical studies, especially those on the same medical condition, use similar or identical eligibility criteria [17]. Therefore, we hypothesize that the generalizability issue might be not only at the level of individual studies, but also at the community level in the entire clinical trial enterprise.…”

GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies

“…The lack of interoperability with different data sources is another concern with current eligibility criteria [4]. Study designers often reuse eligibility criteria from previous clinical trials with minimal modifications [10], which may lead to a concordant bias in sampling and under-representation of certain subgroups. Some other researchers rely on past experience for patient selection.…”