title: Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings.
abstract: BACKGROUND
  Natural Language Processing (NLP) has been shown effective to analyze the content of radiology reports and identify diagnosis or patient characteristics. We evaluate the combination of NLP and machine learning to detect thromboembolic disease diagnosis and incidental clinically relevant findings from angiography and venography reports written in French. We model thromboembolic diagnosis and incidental findings as a set of concepts, modalities and relations between concepts that can be used as features by a supervised machine learning algorithm. A corpus of 573 radiology reports was de-identified and manually annotated with the support of NLP tools by a physician for relevant concepts, modalities and relations. A machine learning classifier was trained on the dataset interpreted by a physician for diagnosis of deep-vein thrombosis, pulmonary embolism and clinically relevant incidental findings. Decision models accounted for the imbalanced nature of the data and exploited the structure of the reports.
  
  
  RESULTS
  The best model achieved an F measure of 0.98 for pulmonary embolism identification, 1.00 for deep vein thrombosis, and 0.80 for incidental clinically relevant findings. The use of concepts, modalities and relations improved performances in all cases.
  
  
  CONCLUSIONS
  This study demonstrates the benefits of developing an automated method to identify medical concepts, modality and relations from radiology reports in French. An end-to-end automatic system for annotation and classification which could be applied to other radiology reports databases would be valuable for epidemiological surveillance, performance monitoring, and accreditation in French hospitals.
authors.affiliation: Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.
authors.forename: Anne-Dominique
authors.initials: AD
authors.lastname: Pham
authors.forename: Aurélie
authors.initials: A
authors.lastname: Névéol
authors.forename: Thomas
authors.initials: T
authors.lastname: Lavergne
authors.forename: Daisuke
authors.initials: D
authors.lastname: Yasunaga
authors.forename: Olivier
authors.initials: O
authors.lastname: Clément
authors.forename: Guy
authors.initials: G
authors.lastname: Meyer
authors.forename: Rémy
authors.initials: R
authors.lastname: Morello
authors.forename: Anita
authors.initials: A
authors.lastname: Burgun
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D033162:Incidental Findings; D009323:Natural Language Processing; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011871:Radiology; D058028:Research Report; D014057:Tomography, X-Ray Computed
references.citation: J Digit Imaging. 2012 Feb;25(1):50-5
references.pmid: 21494902
references.citation: Radiology. 2009 Feb;250(2):327-30
references.pmid: 19188309
references.citation: Summit Transl Bioinform. 2009 Mar 01;2009:1-32
references.pmid: 21347157
references.citation: Arch Intern Med. 2009 Nov 23;169(21):1961-5
references.pmid: 19933956
references.citation: J Biomed Inform. 2013 Apr;46(2):354-62
references.pmid: 23354284
references.citation: Radiology. 2005 Jan;234(1):265-73
references.pmid: 15618385
references.citation: Radiology. 2006 Jul;240(1):256-62
references.pmid: 16720870
references.citation: J Thromb Haemost. 2008 Sep;6(9):1478-81
references.pmid: 18627442
references.citation: N Engl J Med. 2006 Jun 1;354(22):2317-27
references.pmid: 16738268
references.citation: J Am Coll Radiol. 2013 Sep;10(9):672-4
references.pmid: 23816427
references.citation: J Biomed Inform. 2009 Oct;42(5):760-72
references.pmid: 19683066
references.citation: Brief Bioinform. 2014 Mar;15(2):327-40
references.pmid: 23255168
references.citation: J Am Med Inform Assoc. 1994 Mar-Apr;1(2):161-74
references.pmid: 7719797
references.citation: BMC Med Inform Decis Mak. 2011 Oct 26;11:65
references.pmid: 22029629
references.citation: N Engl J Med. 2006 Jun 1;354(22):2383-5
references.pmid: 16738276
references.citation: Ann Emerg Med. 2013 Aug;62(2):162-9
references.pmid: 23548405
references.citation: J Am Coll Radiol. 2010 Oct;7(10):754-73
references.pmid: 20889105
references.citation: Int J Med Inform. 2005 Mar;74(2-4):119-24
references.pmid: 15694616
references.citation: J Biomed Inform. 2011 Oct;44(5):728-37
references.pmid: 21459155
references.citation: J Comput Assist Tomogr. 2008 Mar-Apr;32(2):214-21
references.pmid: 18379305
references.citation: J Biomed Inform. 2006 Apr;39(2):196-208
references.pmid: 16230050
references.citation: Stud Health Technol Inform. 2010;160(Pt 2):949-53
references.pmid: 20841824
references.citation: AJR Am J Roentgenol. 2008 Feb;190(2):322-6
references.pmid: 18212216
references.citation: Stud Health Technol Inform. 2008;136:809-14
references.pmid: 18487831
references.citation: AMIA Annu Symp Proc. 2010 Nov 13;2010:152-6
references.pmid: 21346959
references.citation: J Am Med Inform Assoc. 2010 Sep-Oct;17(5):507-13
references.pmid: 20819853
references.citation: AJR Am J Roentgenol. 2011 May;196(5):1065-72
references.pmid: 21512072
references.citation: AMIA Annu Symp Proc. 2011;2011:501-10
references.pmid: 22195104
references.citation: J Biomed Inform. 2009 Aug;42(4):692-701
references.pmid: 19275946
references.citation: Stud Health Technol Inform. 2004;107(Pt 1):535-9
references.pmid: 15360870
references.citation: AJR Am J Roentgenol. 1998 Apr;170(4):951-4
references.pmid: 9530042
references.citation: J Am Med Inform Assoc. 2010 May-Jun;17(3):229-36
references.pmid: 20442139
references.citation: Proc Annu Symp Comput Appl Med Care. 1995;:347-51
references.pmid: 8563299
references.citation: Database (Oxford). 2013 Apr 12;2013:bat019
references.pmid: 23584833