ArticleJournal of the American Medical Informatics Association : JAMIA2015
Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utilization of EHRs for clinical trials: a systematic review.BMC medical research methodology · 2024Pooled it
- Automated identification of fall-related injuries in unstructured clinical notes.American journal of epidemiology · 2025Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models.NPJ digital medicine · 2024Article
- Distilling large language models for matching patients to clinical trials.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- Targeted electronic health record-based recruitment strategy to enhance COVID-19 vaccine response clinical research study enrollment.Contemporary clinical trials communications · 2024Article
- Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research.Journal of cardiovascular translational research · 2023Review
- Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.BMC musculoskeletal disorders · 2022Review
- Molecular-based precision oncology clinical decision making augmented by artificial intelligence.Emerging topics in life sciences · 2021Review
- Early experience with an opt-in research register - Scottish Health Research Register (SHARE): a multi-method evaluation of participant recruitment performance.BMC medical research methodology · 2021Article
- Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening.ACR open rheumatology · 2021Article
- EMR2vec: Bridging the gap between patient data and clinical trial.Computers & industrial engineering · 2021Article
- Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing.Frontiers in neurology · 2021Article
- Article
- Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation.JMIR medical informatics · 2020Article
- EHR phenotyping for research recruitment: Researcher, IRB, and physician perspectives on approaches to contacting patients.Journal of clinical and translational science · 2020Article
- Digital tools for the recruitment and retention of participants in randomised controlled trials: a systematic map.Trials · 2020Review
- Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research register (SHARE).BMC medical informatics and decision making · 2020Article
- Examining the Use of Real-World Evidence in the Regulatory Process.Clinical pharmacology and therapeutics · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveTo develop a cost-effective, case-based reasoning framework for clinical research eligibility screening by only reusing the electronic health records (EHRs) of minimal enrolled participants to represent the target patient for each trial under consideration. MATERIALS AND
methodsThe EHR data--specifically diagnosis, medications, laboratory results, and clinical notes--of known clinical trial participants were aggregated to profile the "target patient" for a trial, which was used to discover new eligible patients for that trial. The EHR data of unseen patients were matched to this "target patient" to determine their relevance to the trial; the higher the relevance, the more likely the patient was eligible. Relevance scores were a weighted linear combination of cosine similarities computed over individual EHR data types. For evaluation, we identified 262 participants of 13 diversified clinical trials conducted at Columbia University as our gold standard. We ran a 2-fold cross validation with half of the participants used for training and the other half used for testing along with other 30 000 patients selected at random from our clinical database. We performed binary classification and ranking experiments.
resultsThe overall area under the ROC curve for classification was 0.95, enabling the highlight of eligible patients with good precision. Ranking showed satisfactory results especially at the top of the recommended list, with each trial having at least one eligible patient in the top five positions.
conclusionsThis relevance-based method can potentially be used to identify eligible patients for clinical trials by processing patient EHR data alone without parsing free-text eligibility criteria, and shows promise of efficient "case-based reasoning" modeled only on minimal trial participants.
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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.