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Volumn 21, Issue 5, 2014, Pages 893-901

Supervised machine learning and active learning in classification of radiology reports

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CANCER REGISTRY; CLASSIFICATION; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; RADIOLOGY; RADIOLOGY REPORT; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84906313707     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-002516     Document Type: Article
Times cited : (52)

References (27)
  • 1
    • 15944400709 scopus 로고    scopus 로고
    • Automated computer-assisted categorization of radiology reports
    • Thomas BJ, Ouellette H, Halpern EF, et al. Automated computer-assisted categorization of radiology reports. AJR Am J Roentgenol 2005;184:687-90.
    • (2005) AJR Am J Roentgenol , vol.184 , pp. 687-690
    • Thomas, B.J.1    Ouellette, H.2    Halpern, E.F.3
  • 2
    • 12344287992 scopus 로고    scopus 로고
    • Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study
    • Dreyer KJ, Kalra MK, Maher MM, et al. Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study. Radiology 2005;234:323-9.
    • (2005) Radiology , vol.234 , pp. 323-329
    • Dreyer, K.J.1    Kalra, M.K.2    Maher, M.M.3
  • 3
    • 35648989562 scopus 로고    scopus 로고
    • Collection of cancer stage data by classifying free-text medical reports
    • McCowan IA, Moore DC. Collection of cancer stage data by classifying free-text medical reports. J Am Med Inform Assoc 2007;17:736-45.
    • (2007) J Am Med Inform Assoc , vol.17 , pp. 736-745
    • McCowan, I.A.1    Moore, D.C.2
  • 4
    • 77952241648 scopus 로고    scopus 로고
    • Discerning tumor status from unstructured mri reports: completeness of information in existing reports and utility of automated natural language processing
    • Cheng L, Zheng J, Savova G, et al. Discerning tumor status from unstructured mri reports: completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging 2010;23:119-32.
    • (2010) J Digit Imaging , vol.23 , pp. 119-132
    • Cheng, L.1    Zheng, J.2    Savova, G.3
  • 5
    • 68949137209 scopus 로고    scopus 로고
    • Active learning literature survey
    • University of Wisconsin-Madison
    • Settles B. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
    • (2009) Computer Sciences Technical Report 1648
    • Settles, B.1
  • 7
    • 21844444960 scopus 로고    scopus 로고
    • Online choice of active learning algorithms
    • Baram Y, El-Yaniv R, Luz K. Online choice of active learning algorithms. J Mach Learn Res 2004;5:255-91.
    • (2004) J Mach Learn Res , vol.5 , pp. 255-291
    • Baram, Y.1    El-Yaniv, R.2    Luz, K.3
  • 10
    • 0042868698 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res 2002;2:45-66.
    • (2002) J Mach Learn Res , vol.2 , pp. 45-66
    • Tong, S.1    Koller, D.2
  • 13
    • 0022064511 scopus 로고
    • A best possible heuristic for the k-center problem
    • Hochbaum DS, Shmoys DB. A best possible heuristic for the k-center problem. Math Operations Res 1985;10:180-4.
    • (1985) Math Operations Res , vol.10 , pp. 180-184
    • Hochbaum, D.S.1    Shmoys, D.B.2
  • 14
    • 84891645802 scopus 로고    scopus 로고
    • Applying active learning to supervised word sense disambiguation in MEDLINE
    • Chen Y, Cao H, Mei M, et al. Applying active learning to supervised word sense disambiguation in MEDLINE. J Am Med Inform Assoc 2013;20:1001-6.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 1001-1006
    • Chen, Y.1    Cao, H.2    Mei, M.3
  • 15
    • 84888200992 scopus 로고    scopus 로고
    • Applying active learning to high-throughput phenotyping algorithms for electronic health records data
    • Chen Y, Carroll RJ, Hinz ERM, et al. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. J Am Med Inform Assoc 2013;20:253-9.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 253-259
    • Chen, Y.1    Carroll, R.J.2    Hinz, E.R.M.3
  • 16
    • 84872236807 scopus 로고    scopus 로고
    • Active learning for clinical text classification: is it better than random sampling?
    • Figueroa RL, Zeng-Treitler Q, Ngo LH, et al. Active learning for clinical text classification: is it better than random sampling? J Am Med Inform Assoc 2012;19:809-16.
    • (2012) J Am Med Inform Assoc , vol.19 , pp. 809-816
    • Figueroa, R.L.1    Zeng-Treitler, Q.2    Ngo, L.H.3
  • 17
    • 45849122150 scopus 로고    scopus 로고
    • Optimal training sets for Bayesian prediction of MeSH® assignment
    • Sohn S, Kim W, Comeau DC, et al. Optimal training sets for Bayesian prediction of MeSH® assignment. J Am Med Inform Assoc 2008;15:546-53.
    • (2008) J Am Med Inform Assoc , vol.15 , pp. 546-553
    • Sohn, S.1    Kim, W.2    Comeau, D.C.3
  • 20
    • 84957069814 scopus 로고    scopus 로고
    • Text categorization with support vector machines: learning with many relevant features
    • Joachims T. Text categorization with support vector machines: learning with many relevant features. Machine learning: ECML-98. 1998:137-42.
    • (1998) Machine learning: ECML-98 , pp. 137-142
    • Joachims, T.1
  • 21
    • 84906306141 scopus 로고    scopus 로고
    • Unified Medical Language System (UMLS). U.S National Library of Medicine, National Institutes of Health, (accessed Mar 2013).
    • Unified Medical Language System (UMLS). U.S National Library of Medicine, National Institutes of Health. http://www.nlm.nih.gov/research/umls/ (accessed Mar 2013).
  • 22
    • 84906306129 scopus 로고    scopus 로고
    • Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). (accessed March 2013).
    • Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). http://www.ihtsdo.org/snomed-ct/ (accessed March 2013).
  • 23
    • 84906306121 scopus 로고    scopus 로고
    • CRF++. Yet another CRF toolkit. Software available at: (accessed Mar 2013).
    • CRF++. Yet another CRF toolkit. Software available at: http://crfpp.sourceforge.net/ (accessed Mar 2013).
  • 24
    • 4944228528 scopus 로고    scopus 로고
    • A practical guide to support vector classification
    • Department of Computer Science and Information Engineering, National Taiwan University 2003-2010.
    • Hsu CC, Chang CW, Lin CJ. A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan University 2003-2010.
    • Technical report
    • Hsu, C.C.1    Chang, C.W.2    Lin, C.J.3
  • 25
    • 50949133669 scopus 로고    scopus 로고
    • Liblinear: a library for large linear classification
    • Fan RE, Chang KW, Hsieh CJ, et al. Liblinear: a library for large linear classification. J Mach Learn Res 2008;9:1871-4.
    • (2008) J Mach Learn Res , vol.9 , pp. 1871-1874
    • Fan, R.E.1    Chang, K.W.2    Hsieh, C.J.3
  • 26
    • 84871379352 scopus 로고    scopus 로고
    • Reverse active learning for optimising information extraction training production
    • In: Michael Thielscher, Dongmo Zhang, eds. Springer Berlin Heidelberg
    • Nguyen D, Patrick J. Reverse active learning for optimising information extraction training production. In: Michael Thielscher, Dongmo Zhang, eds. AI 2012: advances in artificial intelligence. Springer Berlin Heidelberg, 2012:445-56.
    • (2012) AI 2012: advances in artificial intelligence , pp. 445-456
    • Nguyen, D.1    Patrick, J.2
  • 27
    • 7444256214 scopus 로고    scopus 로고
    • Using cluster-based sampling to select initial training set for active learning in text classification
    • In: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang, eds. Springer Berlin Heidelberg
    • Kang J, Ryu KR, Kwon HC. Using cluster-based sampling to select initial training set for active learning in text classification. In: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang, eds. Advances in knowledge discovery and data mining. Springer Berlin Heidelberg, 2004:384-8.
    • (2004) Advances in knowledge discovery and data mining , pp. 384-388
    • Kang, J.1    Ryu, K.R.2    Kwon, H.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.