ArticleJournal of biomedical informatics2011
Dynamic categorization of clinical research eligibility criteria by hierarchical clustering.
Article in Journal of biomedical informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials.BMC medical informatics and decision making · 2016Pooled it
- Evaluation of Eligibility Criteria Relevance for the Purpose of IT-Supported Trial Recruitment: Descriptive Quantitative Analysis.JMIR formative research · 2024Article
- Implementation of inclusion and exclusion criteria in clinical studies in OHDSI ATLAS software.Scientific reports · 2023Article
- Learning and visualizing chronic latent representations using electronic health records.BioData mining · 2022Article
- Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.BMC medical informatics and decision making · 2021Article
- Analysis of Not Structurable Oncological Study Eligibility Criteria for Improved Patient-Trial Matching.Methods of information in medicine · 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
- Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records.Applied clinical informatics · 2020Article
- Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Correlation Clustering of Stable Angina Clinical Care Patterns for 506 Thousand Patients.Journal of healthcare engineering · 2017Article
- Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data.JMIR medical informatics · 2016Article
- A computational framework for converting textual clinical diagnostic criteria into the quality data model.Journal of biomedical informatics · 2016Article
- Automatic data source identification for clinical trial eligibility criteria resolution.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Developing a modular architecture for creation of rule-based clinical diagnostic criteria.BioData mining · 2016Article
- Visual aggregate analysis of eligibility features of clinical trials.Journal of biomedical informatics · 2015Article
- SimQ: real-time retrieval of similar consumer health questions.Journal of medical Internet research · 2015Article
- Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Clustering clinical trials with similar eligibility criteria features.Journal of biomedical informatics · 2014Article
- A European inventory of common electronic health record data elements for clinical trial feasibility.Trials · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity.
designThe UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hierarchical clustering and to train supervised classifiers. MEASUREMENTS: We induced 27 categories and measured the prevalence of the categories in 27,278 eligibility criteria from 1578 clinical trials and compared the classification performance (i.e., precision, recall, and F1-score) between the UMLS-based feature representation and the "bag of words" feature representation among five common classifiers in Weka, including J48, Bayesian Network, Naïve Bayesian, Nearest Neighbor, and instance-based learning classifier.
resultsThe UMLS semantic feature representation outperforms the "bag of words" feature representation in 89% of the criteria categories. Using the semantically induced categories, machine-learning classifiers required only 2000 instances to stabilize classification performance. The J48 classifier yielded the best F1-score and the Bayesian Network classifier achieved the best learning efficiency.
conclusionThe UMLS is an effective knowledge source and can enable an efficient feature representation for semi-automated semantic category induction and automatic categorization for clinical research eligibility criteria and possibly other clinical text.
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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.