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 multimodal 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 predictiveness 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.

对神经退行性疾病（例如阿尔茨海默氏病（AD）和相关痴呆症）的早期预测可能有助于更早地获得医疗和社会支持。此外，检测患有临床前疾病的个体可能有助于丰富临床试验人群，以研究可改变疾病干预措施的研究。

语言和语言模式的变化可能发生在神经退行性疾病的早期阶段，例如AD和额颞痴呆，并随着疾病的进展而恶化。这导致最近尝试创建通过语言分析预测认知障碍和痴呆的自动方法。先前的工作通过引入一些与任务无关的语言和声学功能，以及一些特定于任务的功能，提高了预测的准确性。但是，特定于任务的功能会阻止模型推广到其他测试和语言。

在本文中，我们专注于探索神经网络模型的有效性，该模型不需要三种特定方式的痴呆症预测就具有特定任务的功能。首先，我们使用多模式神经模型来融合语言特征和声学特征，并与简单地串联这些特征相比来研究性能变化。其次，我们提出了一种由神经一致性模型生成的新颖的一致性特征，并研究了这一新特征对痴呆症预测的可预测性。最后，我们采用了一种不使用特征工程的端到端神经方法，并在广泛使用的痴呆症数据集上实现了最新的分类结果。