{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:53:59Z","timestamp":1790560439246,"version":"4.1.0"},"reference-count":19,"publisher":"Elsevier BV","issue":"9","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacr.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American College of Radiology"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1016\/j.jacr.2019.05.038","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T05:04:20Z","timestamp":1561093460000},"page":"1299-1304","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":24,"special_numbering":"PB","title":["Feasibility of Natural Language Processing\u2013Assisted Auditing of Critical Findings in Chest Radiology"],"prefix":"10.1016","volume":"16","author":[{"given":"Marta E.","family":"Heilbrun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian E.","family":"Chapman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan","family":"Narasimhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neel","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle","family":"Mowery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jacr.2019.05.038_bib1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s10278-011-9426-6","article-title":"Automated detection of critical results in radiology reports","volume":"25","author":"Lakhani","year":"2012","journal-title":"J\u00a0Digit Imaging"},{"key":"10.1016\/j.jacr.2019.05.038_bib2","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.jacr.2013.12.016","article-title":"Actionable findings and the role of IT support: report of the ACR Actionable Reporting Work Group","volume":"11","author":"Larson","year":"2014","journal-title":"J\u00a0Am Coll Radiol"},{"key":"10.1016\/j.jacr.2019.05.038_bib4","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1016\/j.jbi.2011.03.011","article-title":"Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm","volume":"44","author":"Chapman","year":"2011","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2019.05.038_bib5","unstructured":"Gentili A, Chapman BE. Use of pycontext to classify reports containing critical results. Radiological Society of North America (RSNA) 97th Scientific Assembly and Annual Meeting; 2011; Chicago, IL."},{"key":"10.1016\/j.jacr.2019.05.038_bib6","doi-asserted-by":"crossref","first-page":"363","DOI":"10.2307\/2529786","article-title":"An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"10.1016\/j.jacr.2019.05.038_bib7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1002\/pds.3387","article-title":"An automated tool for detecting medication overuse based on the electronic health records","volume":"22","author":"Salmasian","year":"2013","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"10.1016\/j.jacr.2019.05.038_bib8","series-title":"Annotation of critical findings from radiology reports: towards automated communication through the electronic health record","year":"2016"},{"key":"10.1016\/j.jacr.2019.05.038_bib9","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1006\/jbin.2001.1029","article-title":"A\u00a0simple algorithm for identifying negated findings and diseases in discharge summaries","volume":"34","author":"Chapman","year":"2001","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2019.05.038_bib10","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.jbi.2009.05.002","article-title":"ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports","volume":"42","author":"Harkema","year":"2009","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2019.05.038_bib11","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.jbi.2012.12.005","article-title":"A\u00a0text processing pipeline to extract recommendations from radiology reports","volume":"46","author":"Yetisgen-Yildiz","year":"2013","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2019.05.038_bib12","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1186\/1471-2105-15-266","article-title":"Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings","volume":"15","author":"Pham","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.jacr.2019.05.038_bib13","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.jbi.2013.01.006","article-title":"An enhanced CRFs-based system for information extraction from radiology reports","volume":"46","author":"Esuli","year":"2013","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2019.05.038_bib14","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1148\/radiol.2017171115","article-title":"Deep learning to classify radiology free-text reports","volume":"286","author":"Chen","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2019.05.038_bib15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.artmed.2018.11.004","article-title":"Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification","volume":"97","author":"Banerjee","year":"2019","journal-title":"Artif Intell Med"},{"key":"10.1016\/j.jacr.2019.05.038_bib16","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2014; Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.1016\/j.jacr.2019.05.038_bib17","article-title":"Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations (ICLR)","author":"Mikolov","year":"2013","journal-title":"Scottsdale, AZ"},{"key":"10.1016\/j.jacr.2019.05.038_bib19","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1093\/bib\/bbv024","article-title":"Community challenges in biomedical text mining over 10 years: success, failure and the future","volume":"17","author":"Huang","year":"2016","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.jacr.2019.05.038_bib20","doi-asserted-by":"crossref","unstructured":"Snow R, O\u2019Connor B, Jurafsky D, Ng AY. Cheap and fast\u2014but is it good? Evaluating non-expert annotations for natural language tasks. Proceedings of the Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics; Stroudsburg, PA, 2008:254-263.","DOI":"10.3115\/1613715.1613751"},{"key":"10.1016\/j.jacr.2019.05.038_bib21","doi-asserted-by":"crossref","first-page":"e73","DOI":"10.2196\/jmir.2426","article-title":"Web 2.0-based crowdsourcing for high-quality gold standard development in clinical natural language processing","volume":"15","author":"Zhai","year":"2013","journal-title":"J\u00a0Med Internet Res"}],"container-title":["Journal of the American College of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1546144019306386?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1546144019306386?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T06:22:04Z","timestamp":1761546124000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1546144019306386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":19,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["S1546144019306386"],"URL":"https:\/\/doi.org\/10.1016\/j.jacr.2019.05.038","relation":{},"ISSN":["1546-1440"],"issn-type":[{"value":"1546-1440","type":"print"}],"subject":[],"published":{"date-parts":[[2019,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Feasibility of Natural Language Processing\u2013Assisted Auditing of Critical Findings in Chest Radiology","name":"articletitle","label":"Article Title"},{"value":"Journal of the American College of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacr.2019.05.038","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 American College of Radiology","name":"copyright","label":"Copyright"}]}}