ArticleJournal of cardiovascular translational research2017
Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.
Article in Journal of cardiovascular translational research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- A systematic review on natural language processing systems for eligibility prescreening in clinical research.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- Clinical and research applications of natural language processing for heart failure.Heart failure reviews · 2025Review
- 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
- Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.ESC heart failure · 2024Article
- Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.JMIR formative research · 2024Article
- Novel Trial Designs in Heart Failure: Using Digital Health Tools to Increase Pragmatism.Current heart failure reports · 2024Review
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
- Text Classification of Cancer Clinical Trial Eligibility Criteria.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A multicenter program for electronic health record screening for patients with heart failure with preserved ejection fraction: Lessons from the DELIVER-EHR initiative.Contemporary clinical trials · 2022Review
- Review
- Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.BMC musculoskeletal disorders · 2022Review
- Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project.JMIR medical informatics · 2022Article
- Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus.ACR open rheumatology · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Digital tools for the recruitment and retention of participants in randomised controlled trials: a systematic map.Trials · 2020Review
- Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019Article
- Designing Future Clinical Trials in Heart Failure With Preserved Ejection Fraction: Lessons From TOPCAT.Current heart failure reports · 2017Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.
Indexed as
Identifiers
28585184What 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.