ArticleJournal of the American Medical Informatics Association : JAMIA2017
Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.Scientific reports · 2025Article
- Implementation of a rule-based algorithm to find patients eligible for cancer clinical trials.JAMIA open · 2024Article
- Artificial Intelligence in Cardiovascular Clinical Trials.Journal of the American College of Cardiology · 2024Review
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.Implementation science : IS · 2024Article
- Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach.Scientific reports · 2023Article
- Deep learning-based detection of patients with bone metastasis from Japanese radiology reports.Japanese journal of radiology · 2023Article
- Machine Learning in Clinical Trials: A Primer with Applications to Neurology.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Text Classification of Cancer Clinical Trial Eligibility Criteria.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora.BMC medical informatics and decision making · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC medical research methodology · 2022Article
- Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.BMC musculoskeletal disorders · 2022Review
- Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.BMC medical informatics and decision making · 2021Article
- Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer.JCO clinical cancer 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
- Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.JAMIA open · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveTo develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women. MATERIALS AND
methodsWe annotated 891 interventional cancer trials from ClinicalTrials.gov based on their eligibility for human immunodeficiency virus (HIV)-positive patients using their eligibility criteria. These annotations were used to develop classifiers based on regular expressions and machine learning (ML). After evaluating classification of cancer trials for eligibility of HIV-positive patients, we sought to evaluate the generalizability of our approach to more general diseases and conditions. We annotated the eligibility criteria for 1570 of the most recent interventional trials from ClinicalTrials.gov for HIV-positive and pregnancy eligibility, and the classifiers were retrained and reevaluated using these data.
resultsOn the cancer-HIV dataset, the baseline regex model, the bag-of-words ML classifier, and the ML classifier with named entity recognition (NER) achieved macro-averaged F2 scores of 0.77, 0.87, and 0.87, respectively; the addition of NER did not result in a significant performance improvement. On the general dataset, ML + NER achieved macro-averaged F2 scores of 0.91 and 0.85 for HIV and pregnancy, respectively. DISCUSSION AND
conclusionThe eligibility status of specific patient populations, such as persons living with HIV and pregnant women, for clinical trials is of interest to both patients and clinicians. We show that it is feasible to develop a high-performing, automated trial classification system for eligibility status that can be integrated into consumer-facing search engines as well as patient-trial matching systems.
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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.