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首页> 外文期刊>JMLR: Workshop and Conference Proceedings >A Neural Model for Predicting Dementia from Language
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A Neural Model for Predicting Dementia from Language

机译：从语言预测痴呆症的神经模型

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Early prediction of neurodegenerative disorders such as Alzheimer’s disease (AD) and related dementias is important in developing early medical supports and social supports, and may identify ideal stages for testing novel therapeutics aimed at preventing disease progression. Currently, a diagnosis is based on clinical expertise and cognitive screening tests, which have limited accuracy in earlier stages of disease, or invasive and resource-intensive testing, such as lumbar puncture or specialized neuroimaging. Changes in speech and language patterns can occur in dementia in its earliest stages and may worsen as the disease progresses. This has led to recent attempts to create automatic methods that predict dementia through language analysis. In addition to features extracted from language samples, previous works have improved the prediction accuracy by introducing some task-specific features. But task-specific features prevent the model from generalizing to other tests. In this paper, we apply a neural model (Hierarchical Attention Networks) to the dementia prediction task. Remarkably, the model requires no task-specific feature and achieves state-of-the-art classification result on a widely used dementia dataset of spoken language. We also perform a detail analysis to interpret how a prediction is made. Interestingly, the same neural model does not work well on a corpus of written text, suggesting that dementia prediction from language may require different methods depending on the genre of the source language.
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在发展早期医疗支持和社会支持方面是重要的，并且可以确定测试旨在预防疾病进展的新型治疗剂的理想阶段。目前，诊断基于临床专业知识和认知筛查试验，其在早期的疾病阶段或侵入性和资源密集型测试中具有有限的准确性，例如腰椎穿刺或专门的神经影像。语音和语言模式的变化可以在最早的阶段发生痴呆症，并且随着疾病的进展而可能恶化。这导致最近尝试通过语言分析创建预测痴呆症的自动方法。除了从语言样本中提取的功能外，之前的作品还通过引入一些特定的特定功能来提高预测准确性。但任务特定的功能可以防止模型概括到其他测试。在本文中，我们将神经模型（分层注意网络）应用于痴呆症预测任务。值得注意的是，该模型不需要任务特定的功能，并在广泛使用的语痴痴呆症数据集上实现最先进的分类结果。我们还执行详细分析以解释如何进行预测。有趣的是，相同的神经模型对书面文本的语料库不适用于良好的，这表明来自语言的痴呆症预测可能根据源语言的类型来需要不同的方法。

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