ArticleJournal of biomedical informatics2013
A human-computer collaborative approach to identifying common data elements in clinical trial eligibility criteria.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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.
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Who cites it
22 citing papers in PubMed.
- An analysis of published study designs in PubMed prisoner health abstracts from 1963 to 2023: a text mining study.BMC medical research methodology · 2024Review
- 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
- Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse.BMC medical informatics and decision making · 2021Article
- Automated Identification of Common Disease-Specific Outcomes for Comparative Effectiveness Research Using ClinicalTrials.gov: Algorithm Development and Validation Study.JMIR medical informatics · 2021Article
- The My Cancer Genome clinical trial data model and trial curation workflow.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Common data elements of breast cancer for research databases: A systematic review.Journal of family medicine and primary care · 2020Review
- Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records.JAMIA open · 2019Article
- DQueST: dynamic questionnaire for search of clinical trials.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Systematic data ingratiation of clinical trial recruitment locations for geographic-based query and visualization.International journal of medical informatics · 2017Article
- Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries.BMC bioinformatics · 2016Article
- Big Data: the challenge for small research groups in the era of cancer genomics.British journal of cancer · 2015Review
- Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Visual aggregate analysis of eligibility features of clinical trials.Journal of biomedical informatics · 2015Article
- Adaptive semantic tag mining from heterogeneous clinical research texts.Methods of information in medicine · 2015Article
- Trend and Network Analysis of Common Eligibility Features for Cancer Trials in ClinicalTrials.gov.Smart health : international conference, ICSH ... ICSH (Conference : Smart Health) · 2014Article
- ClinicalTrials.gov as a data source for semi-automated point-of-care trial eligibility screening.PloS one · 2014Article
- A method for analyzing commonalities in clinical trial target populations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.Applied clinical informatics · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo identify Common Data Elements (CDEs) in eligibility criteria of multiple clinical trials studying the same disease using a human-computer collaborative approach.
designA set of free-text eligibility criteria from clinical trials on two representative diseases, breast cancer and cardiovascular diseases, was sampled to identify disease-specific eligibility criteria CDEs. In this proposed approach, a semantic annotator is used to recognize Unified Medical Language Systems (UMLSs) terms within the eligibility criteria text. The Apriori algorithm is applied to mine frequent disease-specific UMLS terms, which are then filtered by a list of preferred UMLS semantic types, grouped by similarity based on the Dice coefficient, and, finally, manually reviewed. MEASUREMENTS: Standard precision, recall, and F-score of the CDEs recommended by the proposed approach were measured with respect to manually identified CDEs.
resultsAverage precision and recall of the recommended CDEs for the two diseases were 0.823 and 0.797, respectively, leading to an average F-score of 0.810. In addition, the machine-powered CDEs covered 80% of the cardiovascular CDEs published by The American Heart Association and assigned by human experts.
conclusionIt is feasible and effort saving to use a human-computer collaborative approach to augment domain experts for identifying disease-specific CDEs from free-text clinical trial eligibility criteria.
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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.