Application of dynamic Bayesian networks to risk assessmnet in medicine

• Autorzy: Oniśko A.

Warianty tytułu

PL

Zastosowanie dynamicznych rozkładów Gaussowskich przy pomocy algorytmu ewolucji różnicowej

• Języki publikacji: EN

Abstrakty

EN

Dynamic Bayesian networks (DBNs) offer a framework for explicit modeling of temporal relationships, and are useful as both prognostic and diagnostic tools. In medicine, for example, they can assist in planning treatment options or in clinical management of patients. They have been also widely applied to genomics and proteomics. This paper shows how dynamic Bayesian networks can be used in a risk assessment in medicine and presents an example of an application to cervical cancer screening. The model is a convenient tool for assessing the risk of cervical precancer and invasive cervical cancer over time. These quantitative risk assessments are helpful for establishing the optimal timing of follow-up screening and are the first step toward generating individualized reevaluation scheduling.

PL

Dynamiczne sieci bayesowskie (DBNs) pozwalają na modelowanie zależności czasowych. Modele te są niejednokrotnie używane w prognostyce. Na przykład w medycynie, jako narzędzia do prognozowania czy też planowania terapii. Dynamiczne siecibayesowskie sa˛ też szeroko stosowane w genomice oraz w proteomice. Atrykuł ten opisuje, w jaki sposób dynamiczne sieci bayesowskie mogą być zastosowane w wyznaczaniu ryzyka w medycynie. W pracy przedstawiono przykład zastosowania dynamicznych sieci bayesowskich w profilaktyce raka szyjki macicy. Prezentowany model został zbudowany w oparciu o dwa źródła wiedzy: opinie eksperta oraz dane medyczne. Model ten pozwala na wyznaczanie ryzyka zachorowania na raka szyjki macicy. Wartości ryzyka wyznaczane przez model pozwalają na określenie optymalnego czasu wykonania kolejnych badań przesiewowych oraz na zindywidualizowanie procesu profilaktyki.

Słowa kluczowe

EN

dynamic Bayesian networks; risk assessment in medicine

PL

dynamiczna sieć bayesowska; wyznaczanie ryzyka w medycynie

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Zeszyty Naukowe Politechniki Białostockiej. Informatyka
• Rocznik: 2010
• Tom: Z. 5
• Strony: 35--49
• Opis fizyczny: Bibliogr. 29 poz., rys., wykr.

Twórcy

autor

Oniśko A.

• Politechnika Białostocka, Wydział Informatyki, Białystok

Bibliografia

• [1] Klaus-Peter Adlassnig, Carlo Combi, Amar K. Das, Elpida T. Keravnou, and Giuseppe Pozzi. Temporal representation and reasoning in medicine: Research directions and challenges. Artificial Intelligence in Medicine, 38:101–113, 2006.
• [2] Juan Carlos Augusto. Temporal reasoning for decision support in medicine. Artificial Intelligence in Medicine, 33:1–24, 2005.
• [3] R. Marshall Austin, Agnieszka Oni´sko, and Marek J. Druzdzel. Bayesian network model analysis as a quality control and risk assessment tool in cervical cancer screening. Journal of Lower Genital Tract Disease, 12:153–179, 2008.
• [4] R. Marshall Austin, Agnieszka Oni´sko, and Marek J. Druzdzel. The Pittsburgh Cervical Cancer Screening Model. Cancer Cytopathology, 114:345, 2008.
• [5] R. Marshall Austin, Agnieszka Oni´sko, and Marek J. Druzdzel. The Pittsburgh Cervical Cancer Screening Model. A Risk Assessment Tool. Arch Pathol Lab Med, 134:744–750, 2010.
• [6] Michael A. Bidus, G. Larry Maxwell, Shalini Kulasingam, G. Scott Rose, John C. Elkas, Mildred Chernofsky, and Evan R. Myers. Cost-effectiveness analysis of liquid-based cytology and human papillomavirus testing in cervical cancer screening. Obstetricians and Gynecologists, 107(5):997–1005, 2006.
• [7] Scott B. Cantor, Marianne C. Fahs, Jeanne S. Mandelblatt, Evan R. Myers, and Gillian D. Sanders. Decision science and cervical cancer. Cancer, 98(9):2003– 2008, 2003.
• [8] Philip E. Castle, Mario Sideri, Jose Jeronimo, Diane Solomon, and Mark Schiffman. Risk assessmenmt to guide the prevention of cervical cancer. American Journal of Obstetrics and Gynecology, 197:356.e1–356.e6, 2007.
• [9] Fulvia Ferrazzi, Paola Sebastiani, Isaac S. Kohane, Marco Ramoni, and Riccardo Bellazzi. Dynamic Bayesian networks in modelling cellular systems: A critical appraisal on simulated data. In Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), pages 544–549, Salt Lake City, Utah, USA, 22-23 June 2006.
• [10] S. F. Galan, F. Aguado, F. J. Díez, and J. Mira. NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time. Artificial Intelligence in Medicine, 25:247–264, 2002.
• [11] Sue J. Goldie, Jane J. Kim, and Thomas C. Wright. Cost-effectiveness of human papillomavirus DNA testing for cervical cancer screening in women aged 30 years or more. Obstetricians and Gynecologists, 103(4):619–631, 2004.
• [12] D. Harmanec, T. Y. Leong, S. Sundaresh, K. L. Poh, T. T. Yeo, I. Ng, and T.W. K Lew. Decision analytic approach to severe head injury management. In Proceedings of the 1999 Annual Meeting of the American Medical Informatics Association (AMIA–99), pages 271–275, Washington, D.C., November 6–10 1999.
• [13] Michelle J. Khan, Philip E. Castle, Attila T. Lorincz, Sholom Wacholder, Mark Sherman, David R. Scott, Brenda B. Rush, Andrew G. Glass, and Mark Schiffman. The elevated 10-year risk of cervical precancer and cancer in women with human papillomavirus (HPV) type 16 or 18 and the possible utility of typespecific hpv testing in clinical practice. Journal of the National Cancer Institute, 97(14):1072–79, 2005.
• [14] Jane J. Kim, Thomas C. Wright, and Sue J. Goldie. Cost-effectiveness of alternative triage strategies for atypical squamous cells of undetermined significance. JAMA, 287:2382–90, 2002.
• [15] Aaron A. Klammer, Sheila M. Reynolds, Jeff A. Bilmes, Michael J. MacCoss, and William Stafford Noble. Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification. Bioinformatics, 24:i348–i356, 2008.
• [16] Tze-Yun Leong. Multiple perspective dynamic decision making. Artificial Intelligence, 105:209–261, 1998.
• [17] Nor Ashidi Mat-Isa, Mohd Yusoff Mashor, and Nor Hayati Othman. An automated cervical pre-cancerous diagnostic system. Artificial Intelligence in Medicine, 42:1–11, 2008.
• [18] Evan R. Myers, Douglas C. McCrory, Kavita Nanda, Lori Bastian, and David B. Matchar. Mathematical model for the natural history of human papillomavirus infection and cervical carcinogenesis. American Journal of Epidemiology, 151:1158–1171, 2000.
• [19] Timothy J. O’Leary, Miguel Tellado, Sally-Beth Buckner, Izzat S. Ali, Angelica Stevens, and Curtis W. Ollayos. PAPNET-assisted rescreening of cervical smears. Cost and accuracy compared with a 100 manual rescreening strategy. JAMA, 279(3):1–11, 1998.
• [20] Agnieszka Oni´sko, Marek J. Druzdzel, and Hanna Wasyluk. An experimental comparison of methods for handling incomplete data in learning parameters of Bayesian networks. In S.T. Wierzcho´n M. Kłopotek, M. Michalewicz,
• [21] Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1988.
• [22] L. R. Rabiner. A tutorial in Hidden Markov Models and selected applications in speech recognition. Proc. of the IEEE, 77(2):257–289, 1989.
• [23] Guglielmo Ronco, Nereo Segnan, Paolo Giorgi-Rossi, Marco Zappa, and et. al. Human papillomavirus testing and liquid-based cytology: Results at recruitment from the new technologies for cervical cancer randomized controlled trial. Journal of the National Cancer Institute, 98(11):765–774, 2006.
• [24] Uwe Siebert, Gaby Sroczynska, Peter Hillemanns, Jutta Engel, Roland Stabenow, Christa Stegmaier, Kerstin Voigt, Bernhard Gibis, Dieter Hölzel, and Sue J. Goldie. The German cervical cancer screening model: development and validation of a decision-analytic model for cervical cancer screening in Germany. The European Journal of Public Health, 16(2):185–192, 2006.
• [25] Frank A. Sonnenberg and J. Robert Beck. Markov models in medical decision making: A practical guide. Medical Decision Making, 13:322–338, 1993.
• [26] Marcel A. J. van Gerven, Babs G. Taal, and Peter J. F. Lucas. Dynamic Bayesian neworks as prognostic models for clinical patient management. Journal of Biomedical Informatics, 41:515–529, 2008.
• [27] Yanping Xiang and Kim-Leng Poh. Time-critical dynamic decision modeling in medicine. Computers in Biology and Medicine, 32:85–97, 2002.
• [28] Xin-Qiu Yao, Huaiqiu Zhu, and Zhen-Su She. A dynamic Bayesian network approach to protein secondary structure prediction. BMC Bioinformatics, 9:49– 61, 2008.
• [29] Min Zou and Suzanne D. Conzen. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21(1):71–79, 2005.