ArticleJournal of biomedical informatics2014
Clustering clinical trials with similar eligibility criteria features.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical Trial Generalizability Assessment in the Big Data Era: A Review.Clinical and translational science · 2020Pooled it
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- A knowledge graph of clinical trials ([Formula: see text]).Scientific reports · 2022Article
- Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.Journal of biomedical informatics · 2022Article
- Medical concept normalization in clinical trials with drug and disease representation learning.Bioinformatics (Oxford, England) · 2021Article
- Health Natural Language Processing: Methodology Development and Applications.JMIR medical informatics · 2021Article
- Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.BMC medical informatics and decision making · 2021Article
- A knowledge base of clinical trial eligibility criteria.Journal of biomedical informatics · 2021Article
- Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods.BMC medical informatics and decision making · 2021Article
- [Artificial intelligence based Chinese clinical trials eligibility criteria classification].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Automated Identification of Common Disease-Specific Outcomes for Comparative Effectiveness Research Using ClinicalTrials.gov: Algorithm Development and Validation Study.JMIR medical informatics · 2021Article
- An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- An approach for transgender population information extraction and summarization from clinical trial text.BMC medical informatics and decision making · 2019Article
- A bibliometric analysis of natural language processing in medical research.BMC medical informatics and decision making · 2018Article
- The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Measuring Text Difficulty Using Parse-Tree Frequency.Journal of the Association for Information Science and Technology · 2017Article
- Learning disease relationships from clinical drug trials.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Correlation Clustering of Stable Angina Clinical Care Patterns for 506 Thousand Patients.Journal of healthcare engineering · 2017Article
- Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials.Annals of the New York Academy of Sciences · 2017Article
- GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies.Journal of biomedical informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesTo automatically identify and cluster clinical trials with similar eligibility features.
methodsUsing 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.
resultsFrom 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 8806 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").
conclusionsWe 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.