Bayesian networks for clinical decision support in lung cancer care

PLoS ONE

Volumn 8, Issue 12, 2013, Pages

  Berkan Sesen, M ^a   Nicholson, Ann E ^b   Banares Alcantara, Rene ^a   Kadir, Timor ^c   Brady, Michael ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b MONASH UNIVERSITY   (Australia)

^c Mirada Medical Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; BAYES THEOREM; CANCER STAGING; CAUSAL ATTRIBUTION; CHEMORADIOTHERAPY; CLINICAL DECISION MAKING; COMPARATIVE STUDY; DATA PROCESSING; HUMAN; LEARNING ALGORITHM; LOGISTIC REGRESSION ANALYSIS; LUNG CANCER; MEDICAL EXPERT; PATIENT CARE; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; SURVIVAL PREDICTION; SURVIVAL RATE; TREATMENT PLANNING;

ALGORITHMS; AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; DATABASES, FACTUAL; DECISION SUPPORT TECHNIQUES; DELIVERY OF HEALTH CARE; HUMANS; LUNG NEOPLASMS; NEOPLASM STAGING; PROGNOSIS; REPRODUCIBILITY OF RESULTS;

EID: 84891915402     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0082349     Document Type: Article

References (76)

1. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23:89-109. Available: http://www.ncbi.nlm.nih.gov/pubmed/11470218. doi:10.1016/S0933-3657 (01) 00077-X. PubMed: 11470218. (Pubitemid 32677979)
2. Lavrac N (1999) Selected techniques for data mining in medicine. Artif Intell Med 16:3-23. Available: http://www.ncbi.nlm.nih.gov/pubmed/10225344. doi:10.1016/S0933-3657 (98) 00062-1. PubMed: 10225344. (Pubitemid 29166882)
3. Fleissig A, Jenkins V, Catt S, Fallowfield L (2006) Multidisciplinary teams in cancer care: are they effective in the UK? Lancet Oncol 7:935-943. Available: http://www.ncbi.nlm.nih.gov/pubmed/17081919. doi:10.1016/S1470-2045 (06) 70940-8. PubMed: 17081919. (Pubitemid 44615953)
4. Lucas PJ, van der Gaag LC, Abu-Hanna A (2004) Bayesian networks in biomedicine and health-care. Artif Intell Med 30:201-214. Available: http://www.ncbi.nlm.nih.gov/pubmed/15081072. Accessed 26 August 2011 doi:10.1016/j.artmed.2003.11.001. PubMed: 15081072. (Pubitemid 38471893)
5. Jayasurya K, Fung G, Yu S, Dehing-Oberije C, De Ruysscher D et al. (2010) Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med Phys 37:1401-1407. Available: http://link.aip.org/link/MPHYA6/v37/i4/p1401/ s1&Agg=doi. Accessed 13 July 2012 doi:10.1118/1.3352709. PubMed: 20443461.
6. National Collaborating Centre for Cancer (2011) The diagnosis and treatment of lung cancer (update). Available: http://guidance.nice.org.uk/CG121/ Guidance.
7. Forsberg JA, Eberhardt J, Boland PJ, Wedin R, Healey JH (2011) Estimating survival in patients with operable skeletal metastases: an application of a Bayesian belief network. PLOS ONE 6:e19956. Available: http://www.pubmedcentral. nih.gov/articlerender.fcgi?artid=3094405&tool=pmcentrez&rendertype= abstract. Accessed 25 May 2013 doi:10.1371/journal.pone.0019956. PubMed: 21603644.
8. Pearl J (2000) Causality: Models, Reasoning and Inference. Cambridge Unviersity Press.
9. Korb K, Nicholson A (2010) Bayesian Artificial Intelligence. 2nd ed. Available:. doi:10.1201/b10391
10. Lauritzen S, Spiegelhalter D (1988) Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. J R Stat Soc 50:157-224.
11. Pearl J, Russell S (2000) Bayesian Networks.
12. Heckerman D (1996) A Tutorial on Learning with Bayesian Networks. Redmond.
13. Daly R, Shen Q, Aitken S (2011) Learning Bayesian networks: approaches and issues. Knowl Eng Rev 26:99-157. Available: http://www.journals.cambridge. org/abstract-S0269888910000251. Accessed 4 August 2011 doi:10.1017/ S0269888910000251.
14. Barber D (2011) Bayesian Reasoning and Machine Learning. Available: www.cs.ucl.ac.uk/staff/D. Barber/brml.
15. Flores MJ, Nicholson AE, Brunskill A, Korb KB, Mascaro S (2011) Incorporating expert knowledge when learning Bayesian network structure: a medical case study. Artif Intell Med 53:181-204. Available: http://www.ncbi.nlm. nih.gov/pubmed/21958683. Accessed 1 February 2013 doi:10.1016/j.artmed.2011.08. 004. PubMed: 21958683.
16. Stojadinovic A, Bilchik A, Smith D, Eberhardt JS, Ben et al Ward E. (2013) Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model. Ann Surg Oncol 20:161-174. Available: http://www.ncbi.nlm.nih.gov/pubmed/22899001. Accessed 25 May 2013 doi:10.1245/s10434-012-2555-4. PubMed: 22899001.
17. Dekker A, Dehing-Oberije C, Ruysscher D De, Lambin P, Komati K et al. (2009) Survival Prediction in Lung Cancer Treated with Radiotherapy: Bayesian Networks vs. Support Vector Machines in Handling Missing Data. Int Conf Mach Learn Appl. pp. 494-497. Available: http://ieeexplore.ieee.org/lpdocs/epic03/ wrapper.htm?arnumber=5381445. Accessed 14 March 2012
18. WHO (2012) Cancer fact sheet No 297. Available: http://www.who.int/ mediacentre/factsheets/fs297/en/. Accessed 25 September 2011
19. Cruz JA, Wishart DS (2006) Applications of machine learning in cancer prediction and prognosis. Cancer INFORM 2:59-77. Available: http://www. pubmedcentral.nih.gov/articlerender.fcgi?artid=2675494&tool= pmcentrez&rendertype=abstract. PubMed: 19458758. (Pubitemid 43854386)
20. Rubin D, Burnside E, Shachter R (2005) A Bayesian Network to assist mammography interpretation. Oper Res Heal Care: 695-720. Available: http://www.springerlink.com/index/g444106246k06536.pdf.
21. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B (2006) Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics 22:e184-e190. Available: http://www.ncbi.nlm. nih.gov/pubmed/16873470. Accessed 23 May 2013 doi:10.1093/bioinformatics/btl230. PubMed: 16873470. (Pubitemid 44288285)
22. Cruz-Ramírez N, Acosta-Mesa HG, Carrillo-Calvet H, Nava-Fernández LA, Barrientos-Martínez RE (2007) Diagnosis of breast cancer using Bayesian networks: a case study. Comput Biol Med 37:1553-1564. Available: http://www.ncbi.nlm.nih.gov/pubmed/17434159. Accessed 22 May 2013 doi:10.1016/j.compbiomed.2007.02.003. PubMed: 17434159. (Pubitemid 47371126)
23. Maskery SM, Hu H, Hooke J, Shriver CD, Liebman MN (2008) A Bayesian derived network of breast pathology co-occurrence. J Biomed Inform 41:242-250. Available: http://www.ncbi.nlm.nih.gov/pubmed/18262472. Accessed 15 July 2013 doi:10.1016/j.jbi.2007.12.005. PubMed: 18262472.
24. Gadewadikar J, Sarigul E, Kuljaca O, Zheng Y, Agyepong K et al. (2009) Exploring Bayesian networks for automated breast cancer detection. IEEE Southeastcon. pp. 153-157.
25. Stojadinovic A, Peoples GE, Libutti SK, Henry LR, Eberhardt J et al. (2009) Development of a clinical decision model for thyroid nodules. BMC Surg 9:12. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi? artid=2731077&tool=pmcentrez&rendertype=abstract. Accessed 23 June 2013 doi:10.1186/1471-2482-9-12. PubMed: 19664278.
26. Zhao D, Weng C (2011) Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction. J Biomed Inform 44:859-868. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi? artid=3174321&tool=pmcentrez&rendertype=abstract. Accessed 25 May 2013 doi:10.1016/j.jbi.2011.05.004. PubMed: 21642013.
27. Oh JH, Craft J, Al Lozi R, Vaidya M, Meng Y et al. (2011) A Bayesian network approach for modeling local failure in lung cancer. Phys Med Biol 56:1635-1651. Available: http://www.ncbi.nlm.nih.gov/pubmed/21335651. Accessed 14 March 2012 doi:10.1088/0031-9155/56/6/008. PubMed: 21335651.
28. Lucas PJ, Boot H, Taal BG (1998) Computer-based decision support in the management of primary gastric non-Hodgkin lymphoma. Methods Inf Med 37:206-219. Available: http://www.ncbi.nlm.nih.gov/pubmed/9787619. PubMed: 9787619. (Pubitemid 28450056)
29. Lim E, Baldwin D, Beckles M, Duffy J, Entwisle J et al. (2010) Guidelines on the radical management of patients with lung cancer. Available: http://www.ncbi.nlm.nih.gov/pubmed/20940263.
30. Crinò L, Weder W, van Meerbeeck J, Felip E (2010) Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 21 Suppl 5:v103-115. Available: http://www.ncbi.nlm.nih.gov/pubmed/20555058. Accessed 5 October 2012
31. Ettinger DS et al. (2012) Non-Small Cell Lung Cancer. Available: www.nccn. com.
32. International Classification of Diseases for Oncology (2000) Morphology of Neoplasms, Edition 3. Available: http://www.wolfbane.com/icd/icdo3.htm.
33. IHTSDO (n. d.) About SNOMED CT. Available: http://www.ihtsdo.org/snomed- ct/snomed-ct0/. Accessed 20 August 2010
34. Riaz SP, Lüchtenborg M, Jack RH, Coupland VH, Linklater KM et al. (2012) Variation in surgical resection for lung cancer in relation to survival: population-based study in England 2004-2006. Eur J Cancer 48:54-60. Available: http://www.ncbi.nlm.nih.gov/pubmed/21871792. Accessed 7 February 2013 doi:10.1016/S0959-8049 (12) 70117-X. PubMed: 21871792.
35. Peake MD (2013) Surgical Resection Rates in Lung Cancer Care.
36. Coleman MP, Forman D, Bryant H, Butler J, Richards M (2011) Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995-2007 (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data. Lancet 377:127-138. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi? artid=3018568&tool= pmcentrez&rendertype=abstract. doi:10.1016/S0140-6736 (10) 62231-3. PubMed: 21183212.
37. Holmberg L, Sandin F, Bray F, Richards M, Spicer J et al. (2010) National comparisons of lung cancer survival in England, Norway and Sweden 2001-2004: differences occur early in follow-up. Thorax 65:436-441. Available: http://www.ncbi.nlm.nih.gov/pubmed/20435867. Accessed 22 February 2013 doi:10.1136/thx.2009.124222. PubMed: 20435867.
38. Kotsiantis SB, Kanellopoulos D, Pintelas PE (2006) Data Preprocessing for Supervised Leaning. Int J Comput Sci 1:111-117.
39. Kuschner KW, Malyarenko DI, Cooke WE, Cazares LH, Semmes OJ et al. (2010) A Bayesian network approach to feature selection in mass spectrometry data. BMC Bioinformatics 11:177. Available: http://www.pubmedcentral.nih.gov/ articlerender.fcgi? artid=3098056&tool=pmcentrez&rendertype=abstract. doi: 10.1186/1471-2105-11-177. PubMed: 20377906.
40. Song D, Ek CH, Huebner K, Kragic D (2011) Multivariate discretization for Bayesian Network structure learning in robot grasping. 2011 IEEE Int Conf Robot Autom. pp. 1944-1950. Available: http://ieeexplore.ieee.org/lpdocs/epic03/ wrapper.htm?arnumber=5979666.
41. Colot O, Olivier PC, El Matouat A (1994) Information criteria and abrupt changes in probability laws. Signal Process Theory Appl: 1855-1858.
42. Janssen KJM, Donders AR, Harrell FE, Vergouwe Y, Chen Q, et al. (2010) Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol 63:721-727. Available: http://www.ncbi.nlm.nih.gov/pubmed/ 20338724. Accessed 27 April 2013
43. Schafer JL (1997) Analysis of Incomplete Multivariate Data. Chapman & Hall. Available: http://www.springerlink.com/index/10.1007/BF02680460.
44. Potthoff RF, Tudor GE, Pieper KS, Hasselblad V (2006) Can one assess whether missing data are missing at random in medical studies? Stat Methods Med Res 15:213-234. Available: http://smm.sagepub.com/cgi/doi/10.1191/ 0962280206sm448oa. Accessed 21 May 2013 doi:10.1191/0962280206sm448oa. PubMed: 16768297. (Pubitemid 43833929)
45. Graham JW (2009) Missing data analysis: making it work in the real world. Annu Rev Psychol 60:549-576. Available: http://www.ncbi.nlm.nih.gov/pubmed/ 18652544. Accessed 17 October 2013 doi:10.1146/annurev. psych.58.110405.085530. PubMed: 18652544.
46. Lin J-H, Haug PJ (2008) Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. J Biomed Inform 41:1-14. Available: http://www.ncbi.nlm.nih.gov/pubmed/17625974. Accessed 29 April 2013 doi:10.1016/j.jbi.2007.06.001. PubMed: 17625974.
47. Mitchell TM (2010) Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Machine Learning: 1-17.
48. The PostgreSQL Global Development Group (2012) PostgreSQL JDBC Driver.
49. *. Knowl Represent: 366-372.
50. Andreassen S, Riekehr Ch, Kristensen B, Schønheyder H, Leibovici L (1999) Using probabilistic and decision-theoretic methods in treatment and prognosis modelling. Artif Intell Med 15:121-134. doi:10.1016/S0933-3657 (98) 00048-7. PubMed: 10082177. (Pubitemid 29079476)
51. Van der Gaag LC, Renooij S, Witteman CL, Aleman BM, Taal BG (2002) Probabilities for a probabilistic network: a case study in oesophageal cancer. Artif Intell Med 25:123-148. Available: http://www.ncbi.nlm.nih.gov/pubmed/ 12031603. doi:10.1016/S0933-3657 (02) 00012-X. PubMed: 12031603. (Pubitemid 34553372)
52. Galán SF, Aguado F, Díez FJ, Mira J (2001) NasoNet, Joining Bayesian Networks and Time to Model Nasopharyngeal Cancer Spread. Artif Intell Med: 207-216. (Pubitemid 33301606)
53. Heckerman D, Horvitz E, Nathwani B (1992) Toward Normative Expert Systems: The Pathfinder Project. Methods Inf Med.
54. Helsper EM, Van der Gaag LC (2002) Building Bayesian Networks Through Ontologies. ECAI: 2002
55. Lucas PJ, de Bruijn NC, Schurink K, Hoepelman A (2000) A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU. Artif Intell Med 19:251-279. Available: http://www.ncbi.nlm.nih.gov/pubmed/ 10906615. doi:10.1016/S0933-3657 (00) 00048-8. PubMed: 10906615. (Pubitemid 30445219)
56. Verma T, Pearl J (1992) An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation. Uncertainty in Artificial Intelligence: 323-330.
57. Bouckaert RR (2008). Bayesian Network Classifiers in Weka for Version: 3-5-7.
58. Cooper GF, Herskovits E (1992). A Bayesian Method for the Induction of Probabilistic Networks from Data. 347:309-347.
59. Spiegelhalter D, Dawid A, Lauritzen SL, Cowell R (1993) Bayesian analysis in expert systems. Stat Sci 8:219-282. doi:10.1214/ss/1177010888.
60. Buntine W (1991) Theory refinement in Bayesian Networks. Uncertainty in Artificial Intelligence: 52-60.
61. Murphy K (2001) The Bayes Net Toolbox for MatLab.
62. *. Machine 163:131-163.
63. Madigan D, York J (1995) Bayesian Graphical Models for Discrete Data. Int Stat Rev 63:215-232. doi:10.2307/1403615.
64. Wallace C, Korb K (1999) Causal Discovery via MML. Causal Model. Intell - Data Manag: 89-111.
65. Twardy CR, Nicholson AE, Korb K (2005) Knowledge engineering cardiovascular Bayesian networks from the literature.
66. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P et al. (2009) WEKA. Available: http://www.cs.waikato.ac.nz/ml/weka/citing.html.
67. Rish I (2001) An empirical Study of the naive Bayes classifier. IJCAI.
68. Lewis DD (1998) Naive (Bayes) at forty: The independence assumption in information retrieval. 1398:4-15. doi:10.1007/BFb0026666.
69. Zhang J, Yu KF (1998) What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 280:1690-1691. Available: http://www.ncbi.nlm.nih.gov/pubmed/9832001. doi:10.1001/jama.280.19. 1690. PubMed: 9832001. (Pubitemid 28532993)
70. Le Cessie S, Van Howelingen JC (1992) Ridge Estimators in Logistic Regression. Appl Stat 41:191-201. doi:10.2307/2347628.
71. Quinlan JR (1993) C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufman.
72. Quinlan JR (2006) Improved Use of Continuous Attributes in C4. 5. J Artifcial Intell Res 4:77-90.
73. Jensen F V., Lauritzen SL, Olesen K (1990) Bayesian updating in causal probabilistic networks by local computations. Comput Stat Q 4:269-282
74. Nyberg J, Brian J, Marcot G, Sulyma R (2006) Using Bayesian belief networks in adaptive management. Can J of Res 3116:3104-3116. doi:10.1139/X06-108. (Pubitemid 46866360)
75. Owens DK, Shachter RD, Nease RF (1997) Representation and analysis of medical decision problems with influence diagrams. Med Decis Making 17:241-262. Available: http://www.ncbi.nlm.nih.gov/pubmed/9219185. doi:10.1177/ 0272989X9701700301. PubMed: 9219185. (Pubitemid 27282026)
76. Shachter RD (1988) Probabilistic Inference and Influence Diagrams. Oper Res 36:589-605. doi:1 287/opre.36.4.589.