ArticleEuropean heart journal. Digital health2022
Automated interpretation of stress echocardiography reports using natural language processing.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Hybrid rule-based and on-premises LLM pipeline for extracting CMR and CPET metrics from free-text reports in repaired tetralogy of Fallot.medRxiv : the preprint server for health sciences · 2026Article
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Racial and ethnic disparities in aortic stenosis within a universal healthcare system characterized by natural language processing for targeted intervention.European heart journal. Digital health · 2025Article
- Evaluating large language models in echocardiography reporting: opportunities and challenges.European heart journal. Digital health · 2025Article
- Article
- Machine learning in cardiac stress test interpretation: a systematic review.European heart journal. Digital health · 2024Review
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Stress echocardiography (SE) findings and interpretations are commonly documented in free-text reports. Reusing SE results requires laborious manual reviews. This study aimed to develop and validate an automated method for abstracting SE reports in a large cohort. Methods and results: This study included adult patients who had SE within 30 days of their emergency department visit for suspected acute coronary syndrome in a large integrated healthcare system. An automated natural language processing (NLP) algorithm was developed to abstract SE reports and classify overall SE results into normal, non-diagnostic, infarction, and ischaemia categories. Randomly selected reports ( Conclusions: Natural language processing is an accurate and efficient method for abstracting unstructured SE reports. This approach creates new opportunities for research, public health measures, and care improvement.
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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.