Clinical decision support for ovarian tumor diagnosis using Bayesian models: Results from the IOTA study
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Sabine Van Huffel | Bart De Moor | Olivier Gevaert | Dirk Timmerman | Ben Van Calster | Caroline Van Holsbeke
[1] D. Jurkovic,et al. Simple ultrasound‐based rules for the diagnosis of ovarian cancer , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[2] M. J. Webb,et al. Laparoscopy versus laparotomy in the management of benign unilateral adnexal masses. , 2002, The Journal of the American Association of Gynecologic Laparoscopists.
[3] Adnan Darwiche,et al. Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..
[4] Patrick Neven,et al. Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125. , 2007, Journal of the National Cancer Institute.
[5] Paulo J. G. Lisboa,et al. Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach , 2006, IEEE Transactions on Neural Networks.
[6] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[7] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[8] S Van Huffel,et al. Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression. , 2006, Human reproduction.
[9] Richard E. Neapolitan,et al. Learning Bayesian networks , 2007, KDD '07.
[10] Christopher K. I. Williams,et al. Comparing Bayesian neural network algorithms for classifying segmented outdoor images , 2001, Neural Networks.
[11] T. Bourne,et al. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group , 2000, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[12] Sabine Van Huffel,et al. Evaluation of Imputation Methods in Ovarian Tumor Diagnostic Models Using Generalized Linear Models and Support Vector Machines , 2010, Medical decision making : an international journal of the Society for Medical Decision Making.
[13] Jonathan M. Teich,et al. Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.
[14] Joos Vandewalle,et al. Prognostic importance of degree of differentiation and cyst rupture in stage I invasive epithelial ovarian carcinoma , 2001, The Lancet.
[15] Bart De Moor,et al. Optimizing variable order, selection and cost using a genetic algorithm for modeling ovarian masses with Bayesian networks , 2008 .
[16] Sabine Van Huffel,et al. Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines , 2003, Artif. Intell. Medicine.
[17] T. Bourne,et al. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[18] Johan A. K. Suykens,et al. Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.
[19] Ian T. Nabney,et al. Evaluating the effectiveness of Bayesian feature selection , 2005 .
[20] J. Suykens,et al. Preoperative diagnosis of ovarian tumors using Bayesian kernel‐based methods , 2007, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[21] A. Sperduti,et al. A Neural-Based System for the Automatic Classification and Follow-Up of Diabetic Retinopathies , 2000, Artificial Neural Networks in Biomedicine.
[22] Sabine Van Huffel,et al. Comparing Analytical Decision Support Models Through Boolean Rule Extraction: A Case Study of Ovarian Tumour Malignancy , 2007, ISNN.
[23] Sabine Van Huffel,et al. Inclusion of CA-125 does not improve mathematical models developed to distinguish between benign and malignant adnexal tumors. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[24] S. Van Huffel,et al. The Bayesian approach: a natural framework for statistical modeling , 2007, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[25] Alessio Micheli,et al. Machine Learning Contribution to Solve Prognostic Medical Problems , 2007 .
[26] Alessio Micheli,et al. An introduction to recursive neural networks and kernel methods for cheminformatics. , 2007, Current pharmaceutical design.
[27] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.