Extrapolation of Clinical Data from an Oral Glucose Tolerance Test Using a Support Vector Machine
To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.
[1] Y. Naito, H. Ohno, A. Sano, H. Nakajima, and M. Tomita, "Construction
of a simulation model of diabetes for pathophysiological analysis using
E-Cell System," Genome Informatics, vol. 13, pp. 478-479, Dec. 2002.
[2] K .G. M. M. Alberti and P. Z. Zimmet, "Definition, diagnosis and
classification of diabetes mellitus and its complications. part. 1: diagnosis
and classification of diabetes mellitus. Provisional report of a WHO
Consultation," Diabet Med, vol. 15, no. 7, pp. 539-553, Jul. 1998.
[3] C. Dalla Man, M. Campioni, K. S. Polonsky, R. Basu, R. A. Rizza, G. Toffolo,
and C. Cobelli, "Two-hour seven-sample oral glucose tolerance test
and meal protocol: minimal model assessment of beta-cell responsivity
and insulin sensitivity in nondiabetic individuals," Diabetes, vol. 54, no.
11, pp. 3265-3273, Nov. 2005.
[4] C. Dalla Man, A. Caumo, and C. Cobelli, "The oral glucose minimal
model: estimation of insulin sensitivity from a meal test," IEEE Trans
Biomed Eng, vol. 49, no.5, pp. 419-429, May. 2002.
[5] G. Toffolo, E. Breda, M. K. Cavaghan, D. A. Ehrmann, K. S. Polonsky,
and C. Cobelli, "Quantitative indexes of beta-cell function during graded
up&down glucose infusion from C-peptide minimal models," Am J
Physiol Endocrinol Metab, vol. 280, no. 1, pp. E2-E10, Jan. 2001.
[6] V. Vapnik, S. Golowich, and A. J. Smola, "A support vector method
for function approximation, regression estimation, and signal processing,"
in Advances in Neural Information Processing Systems: Proceedings of
the 1996 conference on Neural Information Processing Systems, vol. 9,
M. C. Mozer, M. I. Jordan, and T. Petsche, Ed. Cambridge: MIT Press,
1997, pp. 281-287.
[7] ISO15197, "In vitro diagnostic test systems: Requirements for bloodglucose
monitoring systems for self-testing in managing diabetes mellitus,"
May. 2003.
[8] C. Dalla Man, M. Camilleri, and C. Cobelli, "A system model of oral
glucose absorption: validation on gold standard data," IEEE Trans Biomed
Eng, vol. 53, no. 12 Pt1, pp. 2472-2478, Dec. 2006.
[1] Y. Naito, H. Ohno, A. Sano, H. Nakajima, and M. Tomita, "Construction
of a simulation model of diabetes for pathophysiological analysis using
E-Cell System," Genome Informatics, vol. 13, pp. 478-479, Dec. 2002.
[2] K .G. M. M. Alberti and P. Z. Zimmet, "Definition, diagnosis and
classification of diabetes mellitus and its complications. part. 1: diagnosis
and classification of diabetes mellitus. Provisional report of a WHO
Consultation," Diabet Med, vol. 15, no. 7, pp. 539-553, Jul. 1998.
[3] C. Dalla Man, M. Campioni, K. S. Polonsky, R. Basu, R. A. Rizza, G. Toffolo,
and C. Cobelli, "Two-hour seven-sample oral glucose tolerance test
and meal protocol: minimal model assessment of beta-cell responsivity
and insulin sensitivity in nondiabetic individuals," Diabetes, vol. 54, no.
11, pp. 3265-3273, Nov. 2005.
[4] C. Dalla Man, A. Caumo, and C. Cobelli, "The oral glucose minimal
model: estimation of insulin sensitivity from a meal test," IEEE Trans
Biomed Eng, vol. 49, no.5, pp. 419-429, May. 2002.
[5] G. Toffolo, E. Breda, M. K. Cavaghan, D. A. Ehrmann, K. S. Polonsky,
and C. Cobelli, "Quantitative indexes of beta-cell function during graded
up&down glucose infusion from C-peptide minimal models," Am J
Physiol Endocrinol Metab, vol. 280, no. 1, pp. E2-E10, Jan. 2001.
[6] V. Vapnik, S. Golowich, and A. J. Smola, "A support vector method
for function approximation, regression estimation, and signal processing,"
in Advances in Neural Information Processing Systems: Proceedings of
the 1996 conference on Neural Information Processing Systems, vol. 9,
M. C. Mozer, M. I. Jordan, and T. Petsche, Ed. Cambridge: MIT Press,
1997, pp. 281-287.
[7] ISO15197, "In vitro diagnostic test systems: Requirements for bloodglucose
monitoring systems for self-testing in managing diabetes mellitus,"
May. 2003.
[8] C. Dalla Man, M. Camilleri, and C. Cobelli, "A system model of oral
glucose absorption: validation on gold standard data," IEEE Trans Biomed
Eng, vol. 53, no. 12 Pt1, pp. 2472-2478, Dec. 2006.
@article{"International Journal of Medical, Medicine and Health Sciences:51503", author = "Jianyin Lu and Masayoshi Seike and Wei Liu and Peihong Wu and Lihua Wang and Yihua Wu and Yasuhiro Naito and Hiromu Nakajima and Yasuhiro Kouchi", title = "Extrapolation of Clinical Data from an Oral Glucose Tolerance Test Using a Support Vector Machine", abstract = "To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.", keywords = "SVM regression, OGTT, diabetes, mathematical model", volume = "3", number = "5", pages = "37-4", }