{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T17:48:03Z","timestamp":1747763283400},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This IRB-approved and HIPAA-compliant study analyzed 1\u00a0702\u00a0462 consecutive reports of 1\u00a0396\u00a0293 patients; patient consent was waived. Reports were analyzed for positive mention of 16\u00a0839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P\u2009&amp;lt;\u20090.05 threshold: edges were evaluated as possible causal relationships. RGO and\/or physician consensus served as ground truth.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>2742 of 16\u00a0839 RGO entities were included, 53\u00a0849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocad119","type":"journal-article","created":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T14:16:56Z","timestamp":1687011416000},"page":"1701-1706","source":"Crossref","is-referenced-by-count":2,"title":["Automated detection of causal relationships among diseases and imaging findings in textual radiology reports"],"prefix":"10.1093","volume":"30","author":[{"given":"Ronnie A","family":"Sebro","sequence":"first","affiliation":[{"name":"Department of Radiology, Department of Orthopedic Surgery, and Center for Augmented Intelligence, Mayo Clinic , Jacksonville, Florida, USA"}]},{"suffix":"Jr","given":"Charles E","family":"Kahn","sequence":"additional","affiliation":[{"name":"Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania , Philadelphia, Pennsylvania, USA"}]}],"member":"286","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"issue":"8","key":"2023092720010601400_ocad119-B1","doi-asserted-by":"publisher","first-page":"e1002141","DOI":"10.1371\/journal.pcbi.1002141","article-title":"Using electronic patient records to discover disease correlations and stratify patient cohorts","volume":"7","author":"Roque","year":"2011","journal-title":"PLoS Comput Biol"},{"issue":"5","key":"2023092720010601400_ocad119-B2","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1093\/jamia\/ocv180","article-title":"Extracting information from the text of electronic medical records to improve case detection: a systematic review","volume":"23","author":"Ford","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2023092720010601400_ocad119-B3","doi-asserted-by":"publisher","first-page":"103276","DOI":"10.1016\/j.jbi.2019.103276","article-title":"Ontology-based clinical information extraction from physician\u2019s free-text notes","volume":"98","author":"Yehia","year":"2019","journal-title":"J Biomed Inform"},{"key":"2023092720010601400_ocad119-B4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.artmed.2015.09.007","article-title":"Information extraction from multi-institutional radiology reports","volume":"66","author":"Hassanpour","year":"2016","journal-title":"Artif Intell Med"},{"issue":"3","key":"2023092720010601400_ocad119-B5","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.jbi.2006.11.003","article-title":"A statistical methodology for analyzing co-occurrence data from a large sample","volume":"40","author":"Cao","year":"2007","journal-title":"J Biomed Inform"},{"key":"2023092720010601400_ocad119-B6","first-page":"106","article-title":"Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics","volume":"2005","author":"Cao","year":"2005","journal-title":"AMIA Annu Symp Proc"},{"key":"2023092720010601400_ocad119-B7","doi-asserted-by":"publisher","first-page":"411","DOI":"10.3233\/shti220487","article-title":"Causal associations among diseases and imaging findings in radiology reports","volume":"294","author":"Sebro","year":"2022","journal-title":"Stud Health Technol Inform"},{"key":"2023092720010601400_ocad119-B8","volume-title":"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference","author":"Pearl","year":"1988"},{"key":"2023092720010601400_ocad119-B9","volume-title":"An Introduction to Bayesian Networks","author":"Jensen","year":"1996"},{"issue":"3","key":"2023092720010601400_ocad119-B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v035.i03","article-title":"Learning Bayesian networks with the bnlearn R package","volume":"35","author":"Scutari","year":"2010","journal-title":"J Stat Soft"},{"issue":"1","key":"2023092720010601400_ocad119-B11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","article-title":"The max-min hill-climbing Bayesian network structure learning algorithm","volume":"65","author":"Tsamardinos","year":"2006","journal-title":"Mach Learn"},{"key":"2023092720010601400_ocad119-B12","first-page":"4","volume-title":"An Overview of the Representation and Discovery of Causal Relationships Using Bayesian Networks. Computation, Causation, and Discovery","author":"Cooper","year":"1999"},{"issue":"1","key":"2023092720010601400_ocad119-B13","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1148\/rg.341135036","article-title":"Radiology Gamuts Ontology: differential diagnosis for the Semantic Web","volume":"34","author":"Budovec","year":"2014","journal-title":"Radiographics"},{"issue":"2","key":"2023092720010601400_ocad119-B14","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/s10278-019-00186-3","article-title":"Integrating an ontology of radiology differential diagnosis with ICD-10-CM, RadLex, and SNOMED CT","volume":"32","author":"Filice","year":"2019","journal-title":"J Digit Imaging"},{"issue":"6","key":"2023092720010601400_ocad119-B15","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1093\/jamia\/ocv020","article-title":"Integrating ontologies of rare diseases and radiological diagnosis","volume":"22","author":"Kahn","year":"2015","journal-title":"J Am Med Inform Assoc"},{"issue":"Web Server issue","key":"2023092720010601400_ocad119-B16","doi-asserted-by":"publisher","first-page":"W170","DOI":"10.1093\/nar\/gkp440","article-title":"BioPortal: ontologies and integrated data resources at the click of a mouse","volume":"37","author":"Noy","year":"2009","journal-title":"Nucleic Acids Res"},{"issue":"7458","key":"2023092720010601400_ocad119-B17","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1136\/bmj.329.7458.168","article-title":"Diagnostic tests 4: Likelihood ratios","volume":"329","author":"Deeks","year":"2004","journal-title":"BMJ"},{"issue":"21","key":"2023092720010601400_ocad119-B18","doi-asserted-by":"publisher","first-page":"425","DOI":"10.21037\/atm.2016.11.11","article-title":"Causal mediation analysis in the context of clinical research","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann Transl Med"},{"issue":"1","key":"2023092720010601400_ocad119-B19","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s43586-021-00092-5","article-title":"Mendelian randomization","volume":"2","author":"Sanderson","year":"2022","journal-title":"Nat Rev Methods Primers"},{"issue":"12","key":"2023092720010601400_ocad119-B20","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1002\/sim.3150","article-title":"A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003","volume":"27","author":"Austin","year":"2008","journal-title":"Stat Med"},{"issue":"2","key":"2023092720010601400_ocad119-B21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1080\/08839514.2018.1526760","article-title":"Bayesian network learning with the PC algorithm: an improved and correct variation","volume":"33","author":"Tsagris","year":"2019","journal-title":"Appl Artif Intell"},{"issue":"6","key":"2023092720010601400_ocad119-B22","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s10278-008-9128-x","article-title":"Use of Radcube for extraction of finding trends in a large radiology practice","volume":"22","author":"Dang","year":"2009","journal-title":"J Digit Imaging"},{"issue":"1","key":"2023092720010601400_ocad119-B23","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10278-011-9425-7","article-title":"Development of automated detection of radiology reports citing adrenal findings","volume":"25","author":"Zopf","year":"2012","journal-title":"J Digit Imaging"},{"issue":"5","key":"2023092720010601400_ocad119-B24","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/jbhi.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"2023092720010601400_ocad119-B25","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TCBB.2018.2849968","article-title":"Natural language processing for EHR-based computational phenotyping","volume":"16","author":"Zeng","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"5","key":"2023092720010601400_ocad119-B26","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1002\/humu.22080","article-title":"Deep phenotyping for precision medicine","volume":"33","author":"Robinson","year":"2012","journal-title":"Hum Mutat"},{"issue":"1","key":"2023092720010601400_ocad119-B27","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ajhg.2018.05.010","article-title":"Deep phenotyping on electronic health records facilitates genetic diagnosis by clinical exomes","volume":"103","author":"Son","year":"2018","journal-title":"Am J Hum Genet"},{"issue":"1","key":"2023092720010601400_ocad119-B28","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1148\/rg.2016150080","article-title":"Natural language processing technologies in radiology research and clinical applications","volume":"36","author":"Cai","year":"2016","journal-title":"Radiographics"},{"key":"2023092720010601400_ocad119-B29","doi-asserted-by":"publisher","first-page":"104779","DOI":"10.1016\/j.ijmedinf.2022.104779","article-title":"Applications of natural language processing in radiology: a systematic review","volume":"163","author":"Linna","year":"2022","journal-title":"Int J Med Inform"},{"issue":"1","key":"2023092720010601400_ocad119-B30","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1148\/radiol.2241011118","article-title":"Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports","volume":"224","author":"Hripcsak","year":"2002","journal-title":"Radiology"},{"issue":"1","key":"2023092720010601400_ocad119-B31","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ijmedinf.2010.10.013","article-title":"Detection of pneumonia using free-text radiology reports in the BioSense system","volume":"80","author":"Asatryan","year":"2011","journal-title":"Int J Med Inform"},{"issue":"5","key":"2023092720010601400_ocad119-B32","doi-asserted-by":"publisher","first-page":"301","DOI":"10.4066\/AMJ.2013.1651","article-title":"Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology","volume":"6","author":"Wagholikar","year":"2013","journal-title":"Australas Med J"},{"key":"2023092720010601400_ocad119-B33","first-page":"1858","article-title":"Classification of hepatocellular carcinoma stages from free-text clinical and radiology reports","volume":"2017","author":"Yim","year":"2017","journal-title":"AMIA Annu Symp Proc"},{"issue":"3","key":"2023092720010601400_ocad119-B34","doi-asserted-by":"publisher","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":"2023092720010601400_ocad119-B35","first-page":"896","article-title":"An ontology-based approach to estimate the frequency of rare diseases in narrative-text radiology reports","volume":"245","author":"Kahn","year":"2017","journal-title":"Stud Health Technol Inform"},{"key":"2023092720010601400_ocad119-B36","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/ICDM.2012.36","author":"Jin","year":"2012"},{"issue":"6","key":"2023092720010601400_ocad119-B37","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1016\/j.jbi.2011.07.001","article-title":"A review of causal inference for biomedical informatics","volume":"44","author":"Kleinberg","year":"2011","journal-title":"J Biomed Inform"},{"issue":"1","key":"2023092720010601400_ocad119-B38","doi-asserted-by":"publisher","first-page":"e12470","DOI":"10.1111\/phc3.12470","article-title":"Causal discovery algorithms: a practical guide","volume":"13","author":"Malinsky","year":"2018","journal-title":"Philos Compass"},{"issue":"5","key":"2023092720010601400_ocad119-B39","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Toward causal representation learning","volume":"109","author":"Sch\u00f6lkopf","year":"2021","journal-title":"Proc IEEE"},{"issue":"1","key":"2023092720010601400_ocad119-B40","doi-asserted-by":"publisher","first-page":"3673","DOI":"10.1038\/s41467-020-17478-w","article-title":"Causality matters in medical imaging","volume":"11","author":"Castro","year":"2020","journal-title":"Nat Commun"},{"issue":"8","key":"2023092720010601400_ocad119-B41","doi-asserted-by":"publisher","first-page":"220638","DOI":"10.1098\/rsos.220638","article-title":"Causal machine learning for healthcare and precision medicine","volume":"9","author":"Sanchez","year":"2022","journal-title":"R Soc Open Sci"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/30\/10\/1701\/51770177\/ocad119.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/30\/10\/1701\/51770177\/ocad119.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T20:38:16Z","timestamp":1695847096000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/30\/10\/1701\/7209869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,6,28]]},"published-print":{"date-parts":[[2023,9,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocad119","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,10,1]]},"published":{"date-parts":[[2023,6,28]]}}}