基于纵向不完整数据联合深度集成回归预测阿尔茨海默病临床评分
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摘要
阿尔茨海默病(AD)是一种进行性神经系统退行疾病,具有不可逆性,需要医生密切监测患者的病情,并根据病情发展及时调整治疗计划。研究表明,临床评分是医生进行疾病评估的最有效依据,且磁共振成像(MRI)数据也非常适合用于预测阿尔茨海默病患者的临床评分。传统研究中,学者们大多是基于单一时间点的MRI数据进行临床评分预测。提出建立一个探索MRI数据与临床评分之间关系的模型,并使用纵向MRI数据预测未来时间点的临床评分。该模型包含3个部分:首先基于相关熵正则化联合学习进行特征选择;然后基于深度多项式网络进行特征编码;最后利用支持向量回归。回归过程在两种情形下进行,情形1是使用基线数据预测未来时间点的临床评分,情形2是结合被测时间点之前的所有数据预测该时间点的临床评分。与此同时,情形2还可填补缺失评分,解决了数据的不完整问题。在情形1中,通过所提出的模型对未来5个不同时间点的临床评分进行预测,获得的平均绝对误差值为2.01、2.06、2.06、2.27、2.00以及皮尔森相关系数值为0.70、0.69、0.56、0.65、0.67。在情形2中,所提出的模型在未来4个不同时间点获得的平均绝对误差值为0.14、0.10、0.09、0.08以及皮尔森相关系数值为0.72、0.75、0.78、0.74。通过以上实验证明,所提出的回归框架不仅可准确描述MRI数据与评分之间的关系,而且可以有效地预测纵向评分。
Alzheimer′s disease(AD) is a neurodegenerative disease with an irreversible and progressive process, and thus close monitoring is essential for making adjustments in the treatment plan. Since clinical scores can indicate the disease status effectively, the prediction of the scores based on the magnetic resonance imaging(MRI) data is highly desirable. Different from previous studies at a single time point, we proposed to build a model to explore the relationship between MRI data and scores, thereby predicting longitudinal scores at future time points from the corresponding MRI data. The model incorporated three parts, correntropy regularized joint learning based features election, deep polynomial network based feature encoding and finally, support vector regression. The regression process was carried out for two scenarios. One was desirable in practice, which is to use baseline data for predictions at future time points, and the other was to further improve the prediction accuracy, which was to combine all the previous data for the prediction at the next time point. Meanwhile, the missing scores were filled in the second scenario to address the incompleteness presented in the data. We predicted longitudinal scores at future time points by the proposed model. Besides, the corresponding average absolute error and Pearson correlation were calculated to estimate the experimental results. In scenario 1, the average absolute value was 2.01, 2.06, 2.06, 2.27, 2.00 and the Pearson correlation coefficient was 0.70, 0.69, 0.56, 0.65, 0.67. In the scenario 2, the average absolute error was 0.14, 0.10, 0.09, 0.08 and the Pearson correlation coefficient is 0.72, 0.75, 0.78, 0.74. The simulation results validated that the proposed model described accurately the relationship between MRI data and scores, and thus was effective in predicting longitudinal scores.
Alzheimer′s disease(AD) is a neurodegenerative disease with an irreversible and progressive process, and thus close monitoring is essential for making adjustments in the treatment plan. Since clinical scores can indicate the disease status effectively, the prediction of the scores based on the magnetic resonance imaging(MRI) data is highly desirable. Different from previous studies at a single time point, we proposed to build a model to explore the relationship between MRI data and scores, thereby predicting longitudinal scores at future time points from the corresponding MRI data. The model incorporated three parts, correntropy regularized joint learning based features election, deep polynomial network based feature encoding and finally, support vector regression. The regression process was carried out for two scenarios. One was desirable in practice, which is to use baseline data for predictions at future time points, and the other was to further improve the prediction accuracy, which was to combine all the previous data for the prediction at the next time point. Meanwhile, the missing scores were filled in the second scenario to address the incompleteness presented in the data. We predicted longitudinal scores at future time points by the proposed model. Besides, the corresponding average absolute error and Pearson correlation were calculated to estimate the experimental results. In scenario 1, the average absolute value was 2.01, 2.06, 2.06, 2.27, 2.00 and the Pearson correlation coefficient was 0.70, 0.69, 0.56, 0.65, 0.67. In the scenario 2, the average absolute error was 0.14, 0.10, 0.09, 0.08 and the Pearson correlation coefficient is 0.72, 0.75, 0.78, 0.74. The simulation results validated that the proposed model described accurately the relationship between MRI data and scores, and thus was effective in predicting longitudinal scores.
引文
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[28] Yuan,Ming,Yi Lin.Model selection and estimation in regression with grouped variables[J].Journal of the Royal Statistical Society:Series B,2006,68(1):49-67.
[2] Castellani R J,Rolston Raj K,Smith M A.Alzheimer disease [J].Disease-A-Month:DM,2010.56(9):484-494.
[3] Bain L J,Jedrziewski K,Morrison-Bogorad M,et al.Healthy brain aging:A meeting report from the sylvan m.cohen annual retreat of the university of pennsylvania institute on aging [J].Alzheimer′s & Dementia,2008.4(6):443-446.
[4] Brookmeyer R,Johnson E,Ziegler-Graham K,et al.Forecasting the global burden of Alzheimer′s disease [J].Alzheimer′s & Dementia,2007,3(3):186-191.
[5] Prince M,Comas-Herrera A,Knapp M,et al.World Alzheimer report 2016:Improving healthcare for people living with dementia:coverage,quality and costs now and in the future [EB/OL].https://www.alz.co.uk/research/world-report-2016,2016-09/2017-10-16.
[6] Pich E M,Jeromin A,Frisoni GB,et al.Imaging as a biomarker in drug discovery for Alzheimer′s disease:Is MRI a suitable technology?[J].Alzheimer′s Research & Therapy,2014.6(4):51.
[7] Zhu Xiaofeng,Suk H,Lee SW,et al.Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification [J].IEEE Transactions on Biomedical Engineering,2016.63(3):607-618.
[8] Jie Biao,Liu Mingxia,Liu Jun,et al.Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer′s disease [J].IEEE Transactions on Biomedical Engineering,2017.64(1):238-249.
[9] Zhu Xiaofeng,Huang Zi,Shen Hengtao,et al.Dimensionality reduction by mixed kernel canonical correlation analysis [J].Pattern Recognition,2012,45(8):3003-3016.
[10] Wang Kaiye,He Ran,Wang Liang,et al.Joint feature selection and subspace learning for cross-modal retrieval [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016.38(10):2010-2023.
[11] Wang Xiaoqian,Shen Dinggang,Huang Heng.Prediction of memory impairment with MRI data:A longitudinal study of Alzheimer′s disease [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention.Shanghai:Springer,2016:273-281.
[12] Zhang Daoqiang,Liu Jun,Shen Dinggang.Temporally-constrained group sparse learning for longitudinal data analysis [J].Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012,2012:264-271.
[13] Zhang Daoqiang,Shen Dinggang,Initiative Alzheimer′s Disease Neuroimaging.Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer′s disease [J].NeuroImage,2012.59(2):895-907.
[14] Livni R,Shalev-Shwartz S,Shamir O.An algorithm for training polynomial networks [J].arXiv preprint:1304.7045,2013.
[15] Shi Jun,Zheng Xiao,Li Yan,et al.Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer′s disease [J].IEEE Journal of Biomedical and Health Informatics,2017,22:173-183.
[16] Liu Xiao,Shi Jun,Zhang Qi.Tumor classification by deep polynomial network and multiple kernel learning on small ultrasound image dataset [C] //International Workshop on Machine Learning in Medical Imaging.Berlin:Springer,2015:313-320.
[17] Shi Jun,Zhou Shichong,Liu Xiao,et al.Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset [J].Neurocomputing,2016,194:87-94.
[18] Greenspan H,Van Ginneken B,Summers R M.Guest editorial deep learning in medical imaging:Overview and future promise of an exciting new technique [J].IEEE Transactions on Medical Imaging,2016,35(5):1153-1159.
[19] Huang Lei,Jin Yan,Gao Yaozong,et al.Longitudinal clinical score prediction in Alzheimer′s disease with soft-split sparse regression based random forest [J].Neurobiology of Aging,2016,46:180-191.
[20] Suk H,Lee S,Shen Dinggang,et al.Deep ensemble learning of sparse regression models for brain disease diagnosis [J].Medical Image Analysis,2017,37:101-113.
[21] Lei Baiying,Yang Peng,Wang Tianfu,et al.Relational-regularized discriminative sparse learning for Alzheimer′s disease diagnosis [J].IEEE Transactions on Cybernetics,2017,47(4):1102-1113.
[22] Zhu Xiaofeng,Li Xuelong,Zhang Shichao,et al.Robust joint graph sparse coding for unsupervised spectral feature selection [J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(6):1263-1275.
[23] Tibshirani R,Saunders M,Rosset S,et al.Sparsity and smoothness via the fused lasso [J].Journal of the Royal Statistical Society:Series B (Statistical Methodology),2005,67(1):91-108.
[24] Nesterov Y.Gradient methods for minimizing composite objective function[J].Mathematical Programming,2013,140(1):125-161.
[25] Gaonkar B,Shinohara RT,Davatzikos C,et al.Interpreting support vector machine models for multivariate group wise analysis in neuroimaging [J].Medical image analysis,2015,24(1):190-204.
[26] Sluimer Jasper D,van der Flier Wiesje M,Karas Giorgos B,et al.Accelerating regional atrophy rates in the progression from normal aging to Alzheimer′s disease [J].European Radiology,2009,19(12):2826.
[27] Misra C,Fan Yong,Davatzikos C.Baseline and longitudinal patterns of brain atrophy in MCI patients,and their use in prediction of short-term conversion to AD:Results from ADNI [J].Neuroimage,2009,44(4):1415-1422.
[28] Yuan,Ming,Yi Lin.Model selection and estimation in regression with grouped variables[J].Journal of the Royal Statistical Society:Series B,2006,68(1):49-67.