{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T20:01:05Z","timestamp":1789156865606,"version":"build-2803163510"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kaiser Permanente Direct Community Benefit Funds"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Echocardiography and cardiac catheterization reports capture important clinical assessment information of cardiac function and disease severity. This study explores using open-source transformer-based language models (LMs) that are run locally within an institutional environment as a privacy-preserving alternative to external API-based large LM to systematically extract clinical data from unstructured echocardiography and cardiac catheterization reports, aiming to improve data accessibility for research and patient care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Two transformer-based LMs, BioclinicalBERT and BART-Large-CNN, were fine-tuned in a secure local environment using a question-answering approach. The dataset included 3286 echocardiography and 1884 cardiac catheterization reports from Kaiser Permanente Southern California\u2019s electronic health records, annotated for 25 and 47 predefined categories, respectively. Three hundred reports from each type were randomly selected and used for validation, with the remainder for training. Model performance was assessed using accuracy, precision, recall, and F1-score at 2 probability thresholds. The effect of training set size on model performance was also evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Both models achieved consistent and high accuracy, precision, and recall (all &amp;gt;90%) across the 5 seed runs for both report types. For echocardiography, BioclinicalBERT reached mean accuracy of 95.7%, precision of 97.6%, recall of 97.4%, and F1-score of 0.98 at the probability threshold of 0.1; BART-Large-CNN had similar results. For cardiac catheterization, BART-Large-CNN slightly outperformed BioclinicalBERT with mean accuracy 94.9% vs 94.3%; precision 96.7% vs 96.3%; recall 96.1% vs 95.7%, and F1-score 0.96 vs 0.96 at the probability threshold of 0.1. Most individual categories showed strong performance, though a few (eg, prosthetic mitral valve, right atrial pressure) had lower scores. Performance improved with more training data, but plateauing around 1000 reports.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and conclusion<\/jats:title>\n                    <jats:p>Fine-tuned transformer-based LMs can effectively extract structured data from unstructured cardiac reports, supporting automated information extraction to enhance research and clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag036","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:37:41Z","timestamp":1774543061000},"source":"Crossref","is-referenced-by-count":2,"title":["Converting unstructured cardiac catheterization and echocardiography reports into structured data using transformer-based language models"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-0490","authenticated-orcid":false,"given":"Fagen","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Sum","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kaiser Permanente Los Angeles Medical Center , Los Angeles, CA 90027,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wansu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek Q","family":"Phan","sequence":"additional","affiliation":[{"name":"Regional Cardiac Catheterization Lab, Kaiser Permanente Los Angeles Medical Center , Los Angeles, CA 90027,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"2026041617101355700_ooag036-B1","doi-asserted-by":"publisher","first-page":"e35","DOI":"10.1161\/CIR.0000000000000932","article-title":"C. 2020 ACC\/AHA guideline for the management of patients with valvular heart disease: executive summary: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"143","author":"Otto","year":"2021","journal-title":"Circulation"},{"key":"2026041617101355700_ooag036-B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.echo.2018.06.004","article-title":"Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography","volume":"32","author":"Mitchell","year":"2019","journal-title":"J Am Soc Echocardiogr"},{"key":"2026041617101355700_ooag036-B3","doi-asserted-by":"publisher","first-page":"e771","DOI":"10.1161\/CIR.0000000000001309","article-title":"2025 ACC\/AHA\/ACEP\/NAEMSP\/SCAI guideline for the management of patients with acute coronary syndromes: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"151","author":"Rao","year":"2025","journal-title":"Circulation"},{"key":"2026041617101355700_ooag036-B4","doi-asserted-by":"publisher","first-page":"e25","DOI":"10.1161\/CIR.0000000000001389","article-title":"Evidence-based practices in the cardiac catheterization laboratory: invasive epicardial coronary physiologic assessment: a scientific statement from the American Heart Association","volume":"153","author":"Bangalore","year":"2025","journal-title":"Circulation"},{"key":"2026041617101355700_ooag036-B5","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1136\/jamia.1994.95236146","article-title":"A general natural-language text processor for clinical radiology","volume":"1","author":"Friedman","year":"1994","journal-title":"J Am Med Inform Assoc"},{"key":"2026041617101355700_ooag036-B6","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.3390\/electronics14153153","article-title":"Large language models for structured and semi-structured data, recommender systems and knowledge base engineering: a survey of recent techniques and architectures","volume":"14","author":"Smaji\u0107","year":"2025","journal-title":"Electronics (Basel)"},{"key":"2026041617101355700_ooag036-B7","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.cvdhj.2021.03.003","article-title":"Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records","volume":"2","author":"Solomon","year":"2021","journal-title":"Cardiovasc Digit Health J"},{"key":"2026041617101355700_ooag036-B8","doi-asserted-by":"publisher","first-page":"e0153749","DOI":"10.1371\/journal.pone.0153749","article-title":"A natural language processing tool for large-scale data extraction from echocardiography reports","volume":"11","author":"Nath","year":"2016","journal-title":"PLoS One"},{"key":"2026041617101355700_ooag036-B9","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.3390\/bioengineering10111307","article-title":"Development and evaluation of a natural language processing system for curating a trans-thoracic echocardiography (TTE) database","volume":"10","author":"Dong","year":"2023","journal-title":"Bioengineering (Basel)"},{"key":"2026041617101355700_ooag036-B10","doi-asserted-by":"publisher","first-page":"e60503","DOI":"10.2196\/60503","article-title":"Identifying the severity of heart valve stenosis and regurgitation among a diverse population within an integrated health care system: natural language processing approach","volume":"8","author":"Xie","year":"2024","journal-title":"JMIR Cardio."},{"key":"2026041617101355700_ooag036-B11","doi-asserted-by":"publisher","first-page":"102242","DOI":"10.1016\/j.jscai.2024.102242","article-title":"From text to data: automatically extracting data from catheterization reports using generative artificial intelligence","volume":"4","author":"Barak-Corren","year":"2025","journal-title":"J Soc Cardiovasc Angiogr Interv"},{"key":"2026041617101355700_ooag036-B12","doi-asserted-by":"publisher","first-page":"e2425981","DOI":"10.1001\/jamanetworkopen.2024.25981","article-title":"Use of generative AI to identify helmet status among patients with micromobility-related injuries from unstructured clinical notes","volume":"7","author":"Burford","year":"2024","journal-title":"JAMA Netw Open"},{"key":"2026041617101355700_ooag036-B13","doi-asserted-by":"publisher","first-page":"e64544","DOI":"10.2196\/64544","article-title":"Using large language models to automate data extraction from surgical pathology report: retrospective cohort study","volume":"9","author":"Lee","year":"2025","journal-title":"JMIR Form Res"},{"key":"2026041617101355700_ooag036-B14","doi-asserted-by":"publisher","first-page":"e65984","DOI":"10.2196\/65984","article-title":"Large language model applications for health information extraction in oncology: scoping review","volume":"11","author":"Chen","year":"2025","journal-title":"JMIR Cancer"},{"key":"2026041617101355700_ooag036-B15","doi-asserted-by":"crossref","first-page":"104662","DOI":"10.1016\/j.jbi.2024.104662","article-title":"Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records","volume":"156","author":"Alkhalaf","year":"2024","journal-title":"J Biomed Inform"},{"key":"2026041617101355700_ooag036-B16","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.7150\/ijms.103332","article-title":"Information extraction from clinical texts with generative pre-trained transformer models","volume":"22","author":"Kim","year":"2025","journal-title":"Int J Med Sci"},{"key":"2026041617101355700_ooag036-B17","first-page":"82","article-title":"LLMs accelerate annotation for medical information extraction","author":"Goel","year":"2023"},{"key":"2026041617101355700_ooag036-B18","first-page":"456","article-title":"Medical information extraction with large language models","author":"Fornasiere","year":"2024"},{"key":"2026041617101355700_ooag036-B19","doi-asserted-by":"publisher","first-page":"ooaf097","DOI":"10.1093\/jamiaopen\/ooaf097","article-title":"Generative artificial intelligence for automated data extraction from unstructured medical text","volume":"8","author":"Dao","year":"2025","journal-title":"JAMIA Open"},{"key":"2026041617101355700_ooag036-B20","article-title":"Publicly available clinical BERT embeddings","author":"Alsentzer","year":"2019"},{"key":"2026041617101355700_ooag036-B21","article-title":"BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension","author":"Lewis","year":"2019"},{"key":"2026041617101355700_ooag036-B22","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1038\/s41746-024-01258-7","article-title":"A framework for human evaluation of large language models in healthcare derived from literature review","volume":"7","author":"Tam","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026041617101355700_ooag036-B23","author":"Facebook\/bart-large-cnn"},{"key":"2026041617101355700_ooag036-B24","author":"Emilyalsentzer\/Bio_ClinicalBERT"},{"key":"2026041617101355700_ooag036-B25","doi-asserted-by":"crossref","first-page":"AIdbp2300110","DOI":"10.1056\/AIdbp2300110","article-title":"CORAL: expert-curated oncology reports to advance language model inference","volume":"1","author":"Sushil","year":"2024","journal-title":"NEJM AI"},{"key":"2026041617101355700_ooag036-B26","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41746-024-01079-8","article-title":"A critical assessment of using ChatGPT for extracting structured data from clinical notes","volume":"7","author":"Huang","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026041617101355700_ooag036-B27","article-title":"Scaling language models: methods, analysis & insights from training gopher","author":"Rae","year":"2021"},{"key":"2026041617101355700_ooag036-B28","article-title":"The Llama 3 Herd of Models","author":"GrattafioriA","year":"2024"},{"key":"2026041617101355700_ooag036-B29","first-page":"26736","article-title":"The stability-efficiency dilemma: investigating sequence length warmup for training GPT models","volume":"35","author":"Li","year":"2022","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["JAMIA Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooag036\/8541953","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooag036\/8541953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:10:22Z","timestamp":1776373822000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooag036\/8541953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3,6]]}},"URL":"https:\/\/doi.org\/10.1093\/jamiaopen\/ooag036","relation":{},"ISSN":["2574-2531"],"issn-type":[{"value":"2574-2531","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2026,3,6]]},"article-number":"ooag036"}}