Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations

  • Baiying Lei
  • Siping Chen
  • Dong NiEmail author
  • Tianfu WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)


Longitudinal prediction of the brain disorder such as Alzheimer’s disease (AD) is important for possible early detection and early intervention. Given the baseline imaging and clinical data, it will be interesting to predict the progress of disease for an individual subject, such as predicting the conversion of Mild Cognitive Impairment (MCI) to AD, in the future years. Most existing methods predicted different clinical scores using different models, or predicted multiple scores at different future time points separately. This often misses the chance of coordinated learning of multiple prediction models for jointly predicting multiple clinical scores at multiple future time points. In this paper, we propose a novel method for joint learning of multiple longitudinal prediction models for multiple clinical scores at multiple future time points. First, for each longitudinal prediction model, we explore three important relationships among training samples, features, and clinical scores, respectively, for enhancing its learning. Then, we further introduce additional relation among different longitudinal prediction models for allowing them to select a common set of features from the baseline imaging and clinical data, with l2,1 sparsity constraint, for their joint training. We evaluate the performance of our joint prediction models with the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, showing much better performance than the state-of-the-art methods in predicting multiple clinical scores at multiple future time points.


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  1. 1.
    Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.-O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage 56(2), 766–781 (2011)CrossRefGoogle Scholar
  2. 2.
    Jack Jr., C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q.: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology 9(1), 119–128 (2010)CrossRefGoogle Scholar
  3. 3.
    Teipel, S.J., Born, C., Ewers, M., Bokde, A.L.W., Reiser, M.F., Möller, H.-J., Hampel, H.: Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. NeuroImage 38(1), 13–24 (2007)CrossRefGoogle Scholar
  4. 4.
    Vemuri, P., Wiste, H.J., Weigand, S.D., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W., Knopman, D.S., Petersen, R.C., Jack Jr., C.R.: MRI and CSF biomarkers in normal, MCI, and AD subjects: Predicting future clinical change. Neurology 73(4), 294–301 (2009)CrossRefGoogle Scholar
  5. 5.
    Zhou, J., Liu, J., Narayan, V.A., Ye, J.: Modeling disease progression via multi-task learning. NeuroImage 78, 233–248 (2013)CrossRefGoogle Scholar
  6. 6.
    Zhang, D., Liu, J., Shen, D.: Temporally-constrained group sparse learning for longitudinal data analysis. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 264–271. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B: Statistical Methodology 68(1), 49–67 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)CrossRefGoogle Scholar
  9. 9.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhu, X., Wu, X., Ding, W., Zhang, S.: Feature selection by joint graph sparse coding. In: SDM 2013, pp. 803–811 (2013)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Biomedical Engineering, School of MedicineShenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingShenzhenChina

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