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.

A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

“…EHRs are also useful for studying distributions of disease indicators [17,18], such as hemoglobin A 1c (HbA 1c ) and serum glucose, in both inpatient and outpatient populations. Meanwhile, the mandatory public registry for clinical trials, ClinicalTrials.gov [19], provides rich information from more than 160,000 clinical trials investigating thousands of diseases, facilitating systematic analysis of the distributions of the characteristics of clinical trial target populations, as reflected in recruitment eligibility criteria, which can be downloaded, parsed, and aggregated [20][21][22][23][24][25][26][27][28].…”

Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text

“…When there is a specific maximum value range defined in DK, strategy (1) is used to enlarge the maximum value range to [min_value/ threshold1, max_value*threshold1] directly. For example, the enlarged range is [2,24] for HbA1C supposing the defined maximum value range is [4,12]. Once there is no pre-defined value range available, Valx calculates the average value associated with the same variable across statements.…”

“…Both the application of a clinical practice guideline on a patient and the recruitment of a research volunteer into a clinical study need to first assess if the patient or the volunteer meets the clinical care or research eligibility criteria, which exist largely as free text in clinical practice guidelines or clinical trial protocols [1][2][3][4][5][6]. Anecdotally over 40% of free-text eligibility criteria contain numeric comparison statements, e.g., "HbA1c superior or equal to 7.5%" and "age eligibility for study: 18 years and older".…”

Visual aggregate analysis of eligibility features of clinical trials

“…We have developed methods for parsing eligibility features from free-text eligibility criteria [17, 27–41] and the derived frequent eligibility features across ClinicalTrials.gov study summaries have produced promising results for searching and indexing studies [29], probing disease relatedness [30], and clustering studies with similar eligibility criteria [17]. Enabled by these techniques, we have created a database of discrete clinical trial eligibility features extracted from ClinicalTrials.gov called COMPACT (Commonalities in Target Populations of Clinical Trials) [42], which allows users to flexibly query sets of clinical studies (e.g., Type 2 diabetes studies) on their shared eligibility features (e.g., HbA1c or BMI) and attributes (e.g., allowed value range for HbA1c or BMI).…”