Observational studyBMC cardiovascular disorders2017
Unlocking echocardiogram measurements for heart disease research through natural language processing.
Observational study in BMC cardiovascular disorders, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multi-ancestry genetic architecture of heart failure subtypes.Nature communications · 2026Pooled it
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Healthcare System Resilience and Adaptation: A 6-Year Analysis of Heart Failure Care in the Veterans Affairs System.Circulation. Heart failure · 2026Article
- 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
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios.Medical sciences (Basel, Switzerland) · 2026Article
- Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.JACC. Advances · 2026Article
- A method to enable clinical and translational research teams with custom real-world data from electronic health record systems.Journal of clinical and translational science · 2026Article
- A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis.medRxiv : the preprint server for health sciences · 2025Article
- Healthcare System Resilience and Adaptation: A Six-Year Analysis of Heart Failure Care in the Veterans Affairs System.medRxiv : the preprint server for health sciences · 2025Article
- Predicted Mortality and Cardiology Follow-up Following Heart Failure Hospitalizations Among Veterans Health Administration Patients.Journal of cardiac failure · 2025Article
- Risk and temporal trends of heart failure subtypes in rheumatoid arthritis.Seminars in arthritis and rheumatism · 2025Article
- EchoLLM: extracting echocardiogram entities with light-weight, open-source large language models.JAMIA open · 2025Article
- Incidence of Right Ventricular Dysfunction in an Echocardiographic Referral Cohort.Journal of the American Heart Association · 2025Article
- Incidence of Pulmonary Hypertension in the Echocardiography Referral Population.Annals of the American Thoracic Society · 2025Article
- 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
- Pre-exposure immunohematologic features of heart failure associate with COVID-19 mortality.NPJ cardiovascular health · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIn order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study. IMPLEMENTATION: A natural language processing system using a dictionary lookup, rules, and patterns was developed to extract heart function measurements that are typically recorded in echocardiogram reports as measurement-value pairs. Curated semantic bootstrapping was used to create a custom dictionary that extends existing terminologies based on terms that actually appear in the medical record. A novel disambiguation method based on semantic constraints was created to identify and discard erroneous alternative definitions of the measurement terms. The system was built utilizing a scalable framework, making it available for processing large datasets.
resultsThe system was developed for and validated on notes from three sources: general clinic notes, echocardiogram reports, and radiology reports. The system achieved F-scores of 0.872, 0.844, and 0.877 with precision of 0.936, 0.982, and 0.969 for each dataset respectively averaged across all extracted values. Left ventricular ejection fraction (LVEF) is the most frequently extracted measurement. The precision of extraction of the LVEF measure ranged from 0.968 to 1.0 across different document types.
conclusionsThis system illustrates the feasibility and effectiveness of a large-scale information extraction on clinical data. New clinical questions can be addressed in the domain of heart failure using retrospective clinical data analysis because key heart function measurements can be successfully extracted using natural language processing.
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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.