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Exploring neural models for predicting dementia from language

机译：探索从语言预测痴呆症的神经模型

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摘要

Early prediction of neurodegenerative disorders such as Alzheimer's disease (AD) and related dementias may facilitate earlier access to medical and social supports. Further, detection of individuals with preclinical disease may help to enrich clinical trial populations for studies examining disease-modifying interventions. Changes in speech and language patterns may occur in the early stages of neurodegenerative diseases such as AD and frontotemporal dementia, with worsening as the disease progresses. This has led to recent attempts to create automatic methods that predict cognitive impairment and dementia through language analysis. Previous works have improved the prediction accuracy by introducing some task-specific features in addition to task-agnostic linguistic and acoustic features. However, task-specific features prevent the model from generalizing to other tests and languages. In this paper, we focus on exploring the effectiveness of neural network models that require no task-specific feature for dementia prediction in three different ways. First, we use a multi-modal neural model to fuse linguistic features and acoustic features, and investigate the performance change compared to simply concatenating these features. Second, we propose a novel coherence feature generated by a neural coherence model, and investigate the predic-tiveness of this new feature for dementia prediction. Finally, we apply an end-to-end neural method which is free from feature engineering and achieves state-of-the-art classification result on a widely used dementia dataset.
机译：早期预测诸如阿尔茨海默病（AD）和相关痴呆症的神经变性障碍可以促进早期获得医疗和社会支持。此外，检测具有临床前疾病的个体可能有助于富集临床试验，用于检查疾病修改干预的研究。语音和语言模式的变化可能发生在诸如广告和胎儿痴呆之类的神经退行性疾病的早期阶段，随着疾病的进展而恶化。这导致最近的尝试通过语言分析创建预测认知障碍和痴呆症的自动方法。除了任务不可行的语言和声学功能之外，还通过引入一些任务特定功能来提高预测准确性。但是，特定于任务特定功能可防止模型概括到其他测试和语言。在本文中，我们专注于探索神经网络模型的有效性，这些模型需要以三种不同的方式对痴呆症预测没有特定于特定的特定特征的特征。首先，我们使用多模态神经模型来熔化语言特征和声学特征，并研究与简单地连接这些功能相比的性能变化。其次，我们提出了一种由神经相干模型产生的新型相干特征，并研究这种新特征对痴呆症预测的新特征的能力。最后，我们应用了一个端到端的神经方法，这些方法没有特征工程，并在广泛使用的痴呆仪数据集上实现最先进的分类结果。

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