Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments
Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments
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DOI：
10.1609/aaai.v34i01.5426
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Lyujian Lu;Saad Elbeleidy;L. Baker;Hua Wang
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作者：
Lyujian Lu;Saad Elbeleidy;L. Baker;Hua Wang
阿尔茨海默病（Alzheimer's Disease，AD）是一种严重影响患者思维、记忆和行为的慢性神经退行性疾病。为了帮助自动AD诊断，已经提出了许多纵向学习模型来预测临床结果和/或疾病状态，然而，这些模型通常没有考虑缺失的患者的时间表型记录，这些记录可以传达AD进展的有价值的信息。AD研究的另一个挑战是如何整合异质性基因型和表型生物标志物以提高诊断预测。为了科普这些挑战，在本文中，我们提出了一种纵向多模态方法，以固定长度向量的格式学习丰富的基因型和表型生物标志物表示，该向量可以同时捕获整个数据集的基线神经成像测量值以及来自不同生物标志物来源的每个参与者随时间推移的随访测量值的不同计数的渐进变化。学习的全局和局部投影通过软约束对齐，并且结构化稀疏性范数用于揭示异质生物标志物测量的多模态结构。虽然提出的目标是明确的动机，以表征AD发展的渐进信息，这是一个非光滑的目标，是难以有效地优化一般。因此，我们得到一个有效的迭代算法，其收敛性是严格保证在数学上。我们使用一种基因型和两种表型生物标志物对阿尔茨海默病神经影像学倡议（ADNI）数据进行了广泛的实验。经验结果已经证明，学习的富集的生物标志物表示在预测各种认知评估的结果方面更有效。此外，我们的模型已经成功地识别了由现有医学发现支持的疾病相关生物标志物，这些发现从临床角度进一步保证了我们方法的正确性。
Alzheimer's Disease (AD) is a chronic neurodegenerative disease that severely impacts patients' thinking, memory and behavior. To aid automatic AD diagnoses, many longitudinal learning models have been proposed to predict clinical outcomes and/or disease status, which, though, often fail to consider missing temporal phenotypic records of the patients that can convey valuable information of AD progressions. Another challenge in AD studies is how to integrate heterogeneous genotypic and phenotypic biomarkers to improve diagnosis prediction. To cope with these challenges, in this paper we propose a longitudinal multi-modal method to learn enriched genotypic and phenotypic biomarker representations in the format of fixed-length vectors that can simultaneously capture the baseline neuroimaging measurements of the entire dataset and progressive variations of the varied counts of follow-up measurements over time of every participant from different biomarker sources. The learned global and local projections are aligned by a soft constraint and the structured-sparsity norm is used to uncover the multi-modal structure of heterogeneous biomarker measurements. While the proposed objective is clearly motivated to characterize the progressive information of AD developments, it is a nonsmooth objective that is difficult to efficiently optimize in general. Thus, we derive an efficient iterative algorithm, whose convergence is rigorously guaranteed in mathematics. We have conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using one genotypic and two phenotypic biomarkers. Empirical results have demonstrated that the learned enriched biomarker representations are more effective in predicting the outcomes of various cognitive assessments. Moreover, our model has successfully identified disease-relevant biomarkers supported by existing medical findings that additionally warrant the correctness of our method from the clinical perspective.