메뉴 건너뛰기




Volumn 43, Issue 4, 2010, Pages 613-622

Learning Bayesian networks from survival data using weighting censored instances

Author keywords

Bayesian network; Medical decision support; Prognostic model; Survival analysis; Weighting censored instances

Indexed keywords

CLINICAL MEDICINE; CONDITIONAL INDEPENDENCES; CONSTRAINT-BASED; COX REGRESSION; DATA SETS; HILL CLIMBING ALGORITHMS; LEARNING BAYESIAN NETWORKS; LEARNING TECHNIQUES; MACHINE LEARNING TECHNIQUES; MEDICAL DECISIONS; MODEL PERFORMANCE; PROGNOSTIC MODEL; SIMULATION STUDIES; SURVIVAL ANALYSIS; SURVIVAL DATA;

EID: 77954144317     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2010.03.005     Document Type: Article
Times cited : (19)

References (47)
  • 2
    • 0000336139 scopus 로고
    • Regression models and life-tables
    • Cox D.R. Regression models and life-tables. J R Stat Soc B (Methodological) 1972, 34(2):187-220.
    • (1972) J R Stat Soc B (Methodological) , vol.34 , Issue.2 , pp. 187-220
    • Cox, D.R.1
  • 5
    • 0344457318 scopus 로고    scopus 로고
    • Prognostic methods in medicine
    • Lucas P., Abu-Hanna A. Prognostic methods in medicine. Artif Intell Med 1999, 15(2):105-119.
    • (1999) Artif Intell Med , vol.15 , Issue.2 , pp. 105-119
    • Lucas, P.1    Abu-Hanna, A.2
  • 8
    • 70350728384 scopus 로고    scopus 로고
    • Impact of censoring on learning Bayesian networks in survival modelling
    • Štajduhar I., Dalbelo-Bašić B., Bogunović N. Impact of censoring on learning Bayesian networks in survival modelling. Artif Intell Med 2009, 47(3):199-217.
    • (2009) Artif Intell Med , vol.47 , Issue.3 , pp. 199-217
    • Štajduhar, I.1    Dalbelo-Bašić, B.2    Bogunović, N.3
  • 9
    • 0343081009 scopus 로고    scopus 로고
    • Machine learning for survival analysis: a case study on recurrence of prostate cancer
    • Zupan B., Demšar J., Kattan M.W., Beck R., Bratko I. Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif Intell Med 2000, 20(1):59-75.
    • (2000) Artif Intell Med , vol.20 , Issue.1 , pp. 59-75
    • Zupan, B.1    Demšar, J.2    Kattan, M.W.3    Beck, R.4    Bratko, I.5
  • 10
    • 33845382806 scopus 로고
    • Nonparametric estimation from incomplete observations
    • Kaplan E.L., Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958, 53:457-481.
    • (1958) J Am Stat Assoc , vol.53 , pp. 457-481
    • Kaplan, E.L.1    Meier, P.2
  • 11
    • 19344364327 scopus 로고    scopus 로고
    • Predicting breast cancer survivability: a comparison of three data mining methods
    • Delen D., Walker G., Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 2005, 34(2):113-127.
    • (2005) Artif Intell Med , vol.34 , Issue.2 , pp. 113-127
    • Delen, D.1    Walker, G.2    Kadam, A.3
  • 12
    • 77954143839 scopus 로고    scopus 로고
    • Expert knowledge and its role in learning Bayesian networks in medicine
    • Lucas P. Expert knowledge and its role in learning Bayesian networks in medicine. Lect Notes Comput Sci 2001, 2101:156-166.
    • (2001) Lect Notes Comput Sci , vol.2101 , pp. 156-166
    • Lucas, P.1
  • 13
    • 0031047117 scopus 로고    scopus 로고
    • Artificial neural networks improve the accuracy of cancer survival prediction
    • Burke H.B., Goodman P.H., Rosen D.B., Henson D.E., Weinstein J.N., Harrell F.E., et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997, 79(4):857-862.
    • (1997) Cancer , vol.79 , Issue.4 , pp. 857-862
    • Burke, H.B.1    Goodman, P.H.2    Rosen, D.B.3    Henson, D.E.4    Weinstein, J.N.5    Harrell, F.E.6
  • 14
    • 0013288412 scopus 로고    scopus 로고
    • Dynamic Bayesian networks: representation, inference and learning
    • Murphy KP. Dynamic Bayesian networks: representation, inference and learning, Ph.D. thesis. University of California; 2002.
    • (2002) Ph.D. thesis. University of California
    • Murphy, K.P.1
  • 16
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper G.F., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992, 9(4):309-347.
    • (1992) Mach Learn , vol.9 , Issue.4 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 17
    • 34249761849 scopus 로고
    • Learning Bayesian networks: the combination of knowledge and statistical data
    • Heckerman D., Geiger D., Chickering D.M. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 1995, 20(3):197-243.
    • (1995) Mach Learn , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 18
    • 0042967741 scopus 로고    scopus 로고
    • Optimal structure identification with greedy search
    • Chickering D.M. Optimal structure identification with greedy search. J Mach Learn Res 2002, 3:507-554.
    • (2002) J Mach Learn Res , vol.3 , pp. 507-554
    • Chickering, D.M.1
  • 19
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
    • Friedman N., Koller D. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach Learn 2003, 50(1):95-125.
    • (2003) Mach Learn , vol.50 , Issue.1 , pp. 95-125
    • Friedman, N.1    Koller, D.2
  • 20
    • 0028482006 scopus 로고
    • Learning Bayesian belief networks: an approach based on the MDL principle
    • Lam W., Bacchus F. Learning Bayesian belief networks: an approach based on the MDL principle. Comput Intell 1994, 10(4):269-293.
    • (1994) Comput Intell , vol.10 , Issue.4 , pp. 269-293
    • Lam, W.1    Bacchus, F.2
  • 23
    • 0036567524 scopus 로고    scopus 로고
    • Learning Bayesian networks from data: an information-theory based approach
    • Cheng J., Greiner R., Kelly J., Bell D., Liu W. Learning Bayesian networks from data: an information-theory based approach. Artif Intell 2002, 137(1-2):43-90.
    • (2002) Artif Intell , vol.137 , Issue.1-2 , pp. 43-90
    • Cheng, J.1    Greiner, R.2    Kelly, J.3    Bell, D.4    Liu, W.5
  • 28
    • 0028148549 scopus 로고
    • Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study
    • Snow P.B., Smith D.S., Catalona W.J. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J Urol 1994, 152(5):1923-1926.
    • (1994) J Urol , vol.152 , Issue.5 , pp. 1923-1926
    • Snow, P.B.1    Smith, D.S.2    Catalona, W.J.3
  • 30
    • 0038162240 scopus 로고    scopus 로고
    • A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer
    • Lisboa P.J.G., Wong H., Harris P., Swindell R. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artif Intell Med 2003, 28(1):1-25.
    • (2003) Artif Intell Med , vol.28 , Issue.1 , pp. 1-25
    • Lisboa, P.J.G.1    Wong, H.2    Harris, P.3    Swindell, R.4
  • 31
    • 0006117317 scopus 로고    scopus 로고
    • Neural networks as statistical methods in survival analysis
    • Cambridge University Press, Cambridge, UK, V. Gant, R. Dybowski (Eds.)
    • Ripley B.D., Ripley R.M. Neural networks as statistical methods in survival analysis. Clinical applications of artificial neural networks 2001, 237-255. Cambridge University Press, Cambridge, UK. V. Gant, R. Dybowski (Eds.).
    • (2001) Clinical applications of artificial neural networks , pp. 237-255
    • Ripley, B.D.1    Ripley, R.M.2
  • 32
    • 47049127967 scopus 로고    scopus 로고
    • Sparse Kernel methods for high-dimensional survival data
    • Evers L., Messow C.-M. Sparse Kernel methods for high-dimensional survival data. Bioinformatics 2008, 24(14):1632-1638.
    • (2008) Bioinformatics , vol.24 , Issue.14 , pp. 1632-1638
    • Evers, L.1    Messow, C.-M.2
  • 33
    • 0344447109 scopus 로고    scopus 로고
    • Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches
    • Sierra B., Larranaga P. Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. Artif Intell Med 1998, 14(1-2):215-230.
    • (1998) Artif Intell Med , vol.14 , Issue.1-2 , pp. 215-230
    • Sierra, B.1    Larranaga, P.2
  • 34
    • 84974667967 scopus 로고    scopus 로고
    • Learning dynamic Bayesian belief networks using conditional phase-type distributions
    • Marshall A., McClean S., Shapcott M., Millard P. Learning dynamic Bayesian belief networks using conditional phase-type distributions. Lect Notes Comput Sci 2000, 516-523.
    • (2000) Lect Notes Comput Sci , pp. 516-523
    • Marshall, A.1    McClean, S.2    Shapcott, M.3    Millard, P.4
  • 35
    • 19944372078 scopus 로고    scopus 로고
    • Generating survival times to simulate Cox proportional hazards models
    • Bender R., Augustin T., Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med 2005, 24:1713-1723.
    • (2005) Stat Med , vol.24 , pp. 1713-1723
    • Bender, R.1    Augustin, T.2    Blettner, M.3
  • 37
    • 1442351098 scopus 로고    scopus 로고
    • A new measure of prognostic separation in survival data
    • Royston P., Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med 2004, 23(5):723-748.
    • (2004) Stat Med , vol.23 , Issue.5 , pp. 723-748
    • Royston, P.1    Sauerbrei, W.2
  • 38
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 2006, 7:1-30.
    • (2006) J Mach Learn Res , vol.7 , pp. 1-30
    • Demšar, J.1
  • 39
    • 0001750957 scopus 로고
    • Approximations of the critical region of the Friedman statistic
    • Iman R.L., Davenport J.M. Approximations of the critical region of the Friedman statistic. Commun Stat Theory Methods 1980, 9(6):571-595.
    • (1980) Commun Stat Theory Methods , vol.9 , Issue.6 , pp. 571-595
    • Iman, R.L.1    Davenport, J.M.2
  • 41
    • 0028080742 scopus 로고
    • Randomized 2×2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German breast cancer study group
    • Schumacher M., Bastert G., Bojar H., Hubner K., Olschewski M., Sauerbrei W., et al. Randomized 2×2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German breast cancer study group. J Clin Oncol 1994, 12(10):2086-2093.
    • (1994) J Clin Oncol , vol.12 , Issue.10 , pp. 2086-2093
    • Schumacher, M.1    Bastert, G.2    Bojar, H.3    Hubner, K.4    Olschewski, M.5    Sauerbrei, W.6
  • 42
    • 27644559962 scopus 로고    scopus 로고
    • R: a Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, AT. Available from: [accessed 31.12.2009]
    • R Development Core Team. R: a Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, AT. Available from: ; 2008 [accessed 31.12.2009]. http://www.R-project.org.
    • (2008) R Development Core Team
  • 43
    • 0032626964 scopus 로고    scopus 로고
    • An application of changepoint methods in studying the effect of age on survival in breast cancer
    • Contal C., O'Quigley J. An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 1999, 30(3):253-270.
    • (1999) Comput Stat Data Anal , vol.30 , Issue.3 , pp. 253-270
    • Contal, C.1    O'Quigley, J.2
  • 47
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 1998, 10(7):1895-1923.
    • (1998) Neural Comput , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.