Development of a kernel function for clinical data

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Volumn , Issue , 2009, Pages 5913-5917

  Daemen, Anneleen ^a   De Moor, Bart ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

INDUSTRIAL ENGINEERING;

APPROPRIATE MODELS; CLINICAL INFORMATION; CLINICAL MANAGEMENT; HIGH THROUGHPUT TECHNOLOGY; KERNEL FUNCTION; LEAST SQUARES SUPPORT VECTOR MACHINES; LINEAR KERNEL FUNCTIONS; MEDICAL HISTORY;

ENGINEERING;

EID: 77950979688     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5334847     Document Type: Conference Paper

References (16)

1. Adjouadi M, Zong N, Ayala M. (2005) Multidimensional pattern recognition and classification of white blood cells using support vector machines. Part Part Syst Charact, 22, 107-118.
2. Bottomley C, Daemen A, et al. (2009) Functional linear discriminant analysis: a new longitudinal approach to the assessment of embryonic growth. Hum Reprod, 24, 278-283.
3. Condous G, Okaro E, et al. (2004) The use of a new logistic regression model for predicting the outcome of pregnancies of unknown location. Hum Reprod, 19, 1900-1910.
4. Edén P, Ritz C, et al. (2004). "Good old" clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur J Can, 40, 1837-1841.
5. Gevaert O, De Smet F, et al. (2006) Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression. Hum Reprod, 21, 1824-1831.
6. Hanley JA, McNeil BJ. (1983) A method of comparing the areas under receiver operating characteristics curves derived from the same cases. Radiology, 148, 839-843.
7. Cawley GC. (2006) Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. Proc. of IJCNN, 1661-1668.
8. Majumder SK, Ghosh N, Gupta PK. (2005) Relevance vector machine for optical diagnosis of cancer. Lasers Surg Med, 36, 323-333.
9. Schölkopf B, Tsuda K, Vert JP. (2004) Kernel methods in computational biology, MIT Press, United States.
10. Shawe-Taylor J, Cristianini N. (2004) Kernel methods for pattern analysis, Cambridge University Press, Cambridge.
11. Suykens JAK, Vandewalle J. (1999) Least Squares Support Vector Machine classifiers, Neural Processing Letters, 9, 293-300.
12. Suykens JAK, Van Gestel T, et al. (2002) Least Squares Support Vector Machines, World Scientific, Singapore.
13. Van Calster B, Timmerman D, et al. (2007) Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods. Ultrasound Obstet Gynecol, 29, 496-504.
14. Van den Bosch T, Daemen A, et al. (2007) Mathematical decision trees versus clinician based algorithms in the diagnosis of endometrial disease. Ultrasound Obstet Gynecol, 30, 412.
15. Vapnik V. (1998) Statistical Learning Theory, Wiley, New York.
16. van de Vijver M, He Y, et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med, 347, 1999-2009.