Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2009-07-21
2011-12-13
Starks, Wilbert L (Department: 2129)
Data processing: artificial intelligence
Machine learning
C706S045000
Reexamination Certificate
active
08078554
ABSTRACT:
Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.
REFERENCES:
patent: 7844560 (2010-11-01), Krishnan et al.
patent: 2003/0120458 (2003-06-01), Rao et al.
Verduijn, et al., Prognostic Bayesian networks I: Rationale, learning procedure, and clinical use, Journal of Biomedical Informatics, vol. 40, Issue 6, Dec. 2007, pp. 609-618.
Peek, et al., ProCarSur: A System for Dynamic Prognostic Reasoning in Cardiac Surgery, Artificial Intelligence in Medicine, Lecture Notes in Computer Science, 2007, vol. 4594/2007, pp. 336-340.
Pfister DG, Johnson DH, Azzoli CG, et al. American Society of Clinical Oncology treatment of unresectable non-small-cell lung cancer guideline: update 2003. J Clin Oncol 2004;22:330-353.
Dehing-Oberije C, De Ruysscher D, van der Weide H, et al. Tumor Volume Combined With Number of Positive Lymph Node Stations Is a More Important Prognostic Factor Than TNM Stage for Survival of Non-Small-Cell Lung Cancer Patients Treated With (Chemo)radiotherapy. Int J Radiat Oncol Biol Phys 2008;70:1039-1044.
Dehing-Oberije C, Yu S, De Ruysscher D, et al. Development and external validation of a prognostic model for 2-year survival of non-small cell lung cancer patients treated with (chemo) radiotherapy. Int J Radiat Oncol Bid Phys (accepted Aug. 2008).
Brundage MD, Davies D, Mackillop WJ. Prognostic factors in non-small cell lung cancer: a decade of progress. Chest 2002;122:1037-1057.
D Heckerman, D Geiger, DM Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 1995—Springer.
Chickering DM, Heckerman D, Meek C. Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research 5 (2004) 1287-1330.
W Lam, F Bacchus. Learning Bayesian Belief networks: An approach based on MDL principle. Computational Intelligence, 1994.
Murphy K. The Bayes Net Toolbox for Matlab, Computing Science and Statistics, vol. 33, No. 2, (2001), pp. 1024-1034.
K. Pelckmans and J. De Brabanter and J. A. K. Suykens and B. De Moor. Handling missing values in support vector machine classifiers. Neural Netwoks Journal. (108) 5-6. 2005.
Alireza Farhangfar and Lukasz Kurgan and Jennifer Dy. Impact of imputation of missing values on classification error for discrete data. Pattern Recognition. (41) 12 . 2008.
Eaton D, Murphy K. Bayesian structure learning using dynamic programming and MCMC. 2007 Proceedings of the 23nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-07).
Pillot G, Siegel BA, Govindan R. Prognostic value of fluorodeoxyglucose positron emission tomography in non-small cell lung cancer: a review. J Thorac Oncol. Feb. 2006;1(2):152-9.
Heckerman D (1998). A tutorial on learning with Bayesian networks. In:Jordan MI, editor. Learning in graphical models. Dordrecht: Kluwer Academic. pp. 301-354.
G. Fung and O. L. Mangasarian. Finite {N}ewton Method for {L}agrangian Support Vector Machine Classification. Special Issue on Support Vector Machines. Neurocomputing, vol. 55, Issues 1-2, Sep. 2003, pp. 39-55.
K. Murphy, “Bayes Net Toolbox for Matlab”, Oct. 19, 2007; http://www.sc.ubc.ca/˜murphyk/Software/BNT/bnt.html.
Burnside ES. Bayesian networks: computer-assisted diagnosis support in radiology. Acad Radiol. Apr. 2005;12(4):422-30.
Commonly diagnosed cancers worldwide. Cancer Research UK (Apr. 2005).
Hoot N, Aronsky D. Using Bayesian networks to predict survival of liver transplant patients. AMIA Annu Symp Proc. 2005:345-9.
Needham, et al., A primer on learning in Bayesian networks for computational biology. PLOS computational biology, vol. 3, issue 8, pp. 1409-1416, 2007.
SL Lauritzen. The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 1995.
AJ Hartemink, DK Gifford, TS Jaakkola, RA Young. Bayesian Methods for Elucidating Genetic Regulatory Networks. IEEE Intelligent Systems, 2002.
Dehing-Oberije Cary
Dekker Andreas Lubbertus Aloysius Johannes
Fung Glenn
Komati Kartik Jayasurya
Lambin Philippe
Ryan Joshua B.
Siemens Medical Solutions USA , Inc.
Starks Wilbert L
LandOfFree
Knowledge-based interpretable predictive model for survival... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Knowledge-based interpretable predictive model for survival..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Knowledge-based interpretable predictive model for survival... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4259177