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Modeling disease progression via multi-task learning

机译：通过多任务学习对疾病进展进行建模

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Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the disease has recently received increasing attentions in AD research. An accurate prediction of disease progression would facilitate optimal decision-making for clinicians and patients. A definitive diagnosis of AD requires autopsy confirmation, thus many clinical/cognitive measures including Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog) have been designed to evaluate the cognitive status of the patients and used as important criteria for clinical diagnosis of probable AD. In this paper, we consider the problem of predicting disease progression measured by the cognitive scores and selecting biomarkers predictive of the progression. Specifically, we formulate the prediction problem as a multi-task regression problem by considering the prediction at each time point as a task and propose two novel multi-task learning formulations. We have performed extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, we use the baseline MRI features to predict MMSE/ADAS-Cog scores in the next 4. years. Results demonstrate the effectiveness of the proposed multi-task learning formulations for disease progression in comparison with single-task learning algorithms including ridge regression and Lasso. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression. We observe that cortical thickness average of left middle temporal, cortical thickness average of left and right Entorhinal, and white matter volume of left Hippocampus play significant roles in predicting ADAS-Cog at all time points. We also observe that several MRI biomarkers provide significant information for predicting MMSE scores for the first 2. years, however very few are shown to be significant in predicting MMSE score at later stages. The lack of predictable MRI biomarkers in later stages may contribute to the lower prediction performance of MMSE than that of ADAS-Cog in our study and other related studies.
机译：阿尔茨海默氏病（AD）是最常见的痴呆类型，是一种严重的神经退行性疾病。识别可追踪疾病进展的生物标志物最近在AD研究中受到越来越多的关注。疾病进展的准确预测将有助于临床医生和患者的最佳决策。明确的AD诊断需要尸检确认，因此，许多临床/认知措施（包括迷你精神状态检查（MMSE）和阿尔茨海默氏病评估量表认知子量表（ADAS-Cog））已被设计用来评估患者的认知状况，并且被认为是重要的可能的AD的临床诊断标准。在本文中，我们考虑了通过认知评分预测疾病进展并选择可预测进展的生物标志物的问题。具体来说，我们通过将每个时间点的预测视为一项任务，将预测问题表述为多任务回归问题，并提出两种新颖的多任务学习公式。我们使用阿尔茨海默氏病神经影像学计划（ADNI）的数据进行了广泛的实验。具体来说，我们使用基线MRI功能来预测未来4年的MMSE / ADAS-Cog评分。结果表明，与包括岭回归和套索的单任务学习算法相比，所提出的针对疾病进展的多任务学习公式是有效的。我们还执行纵向稳定性选择，以识别和分析疾病进展中生物标志物的时间模式。我们观察到，在所有时间点，左中间颞叶的皮质平均厚度，左内角和右内嗅的皮质平均厚度以及海马的白质体积在预测ADAS-Cog方面均起着重要作用。我们还观察到，几种MRI生物标记物为预测前2年的MMSE分数提供了重要的信息，但是很少有人被证明在以后的MMSE分数预测中具有重要意义。在我们的研究和其他相关研究中，在后期缺乏可预测的MRI生物标记物可能导致MMSE的预测性能低于ADAS-Cog的预测性能。

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