ArticlePloS one2016
A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging.Pediatric radiology · 2026Review
- Leveraging natural language processing artificial intelligence for automated data extraction of ejection fraction and strain from cardiovascular imaging reports.American heart journal plus : cardiology research and practice · 2026Article
- Converting unstructured cardiac catheterization and echocardiography reports into structured data using transformer-based language models.JAMIA open · 2026Article
- Benchmarking large language models for clinical data extraction from Portuguese medical notes in a university hospital.Cadernos de saude publica · 2026Article
- Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.JACC. Advances · 2026Article
- A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Evaluating large language models in echocardiography reporting: opportunities and challenges.European heart journal. Digital health · 2025Article
- Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?Pathogens (Basel, Switzerland) · 2025Review
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.Scientific reports · 2025Article
- Article
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
- Current role and future perspectives of artificial intelligence in echocardiography.World journal of cardiology · 2023Review
- Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors.Pediatric blood & cancer · 2023Article
- Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.BMC medical informatics and decision making · 2022Article
- The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging.Pediatric radiology · 2022Review
- One Clinician Is All You Need-Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development.JMIR medical informatics · 2022Article
- Cardiovascular informatics: building a bridge to data harmony.Cardiovascular research · 2022Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92-99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.
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Registered trials
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