eTACTS: a method for dynamically filtering clinical trial search results

Abstract

Objective: Information overload is a significant problem facing online clinical trial searchers. We present eTACTS, a novel interactive retrieval framework using common eligibility tags to dynamically filter clinical trial search results.

Materials and methods: eTACTS mines frequent eligibility tags from free-text clinical trial eligibility criteria and uses these tags for trial indexing. After an initial search, eTACTS presents to the user a tag cloud representing the current results. When the user selects a tag, eTACTS retains only those trials containing that tag in their eligibility criteria and generates a new cloud based on tag frequency and co-occurrences in the remaining trials. The user can then select a new tag or unselect a previous tag. The process iterates until a manageable number of trials is returned. We evaluated eTACTS in terms of filtering efficiency, diversity of the search results, and user eligibility to the filtered trials using both qualitative and quantitative methods.

Results: eTACTS (1) rapidly reduced search results from over a thousand trials to ten; (2) highlighted trials that are generally not top-ranked by conventional search engines; and (3) retrieved a greater number of suitable trials than existing search engines.

Discussion: eTACTS enables intuitive clinical trial searches by indexing eligibility criteria with effective tags. User evaluation was limited to one case study and a small group of evaluators due to the long duration of the experiment. Although a larger-scale evaluation could be conducted, this feasibility study demonstrated significant advantages of eTACTS over existing clinical trial search engines.

Conclusion: A dynamic eligibility tag cloud can potentially enhance state-of-the-art clinical trial search engines by allowing intuitive and efficient filtering of the search result space.

Keywords: Association rules; Clinical trials; Dynamic information filtering; Information storage and retrieval; Interactive information retrieval; Tag cloud.

Figure 1

Overview of the eTACTS framework.

Figure 2

Eligibility tag cloud derived from results associated to the search “diabetes mellitus”.

Figure 3

Example of a mock patient profile adopted in the user evaluation.

Figure 4

The mean number of clinical trials returned after a certain number of selected tags averaged over 50 distinct query conditions (each tested with 500 simulations based on random tag clicks). We compare tag cloud updating strategies based on most frequent tag sampling (“tagmost”), last selected tag context (“jc-context”), and association rules (“a-rule”).