从转录语音预测阿尔茨海默病的想法密度
Idea density for predicting Alzheimer's disease from transcribed speech
摘要：
思想密度（ID）测量思想或基本谓词在话语或文本中表达的速率。发现较低的ID与发展阿尔茨海默病（AD）的风险增加有关（Snowdon等人，1996; Engelman等人，2010年）。ID有两个不同的版本：命题思想密度（PID）计算表达的思想，可以应用于任何文本，而语义思想密度（SID）计算预定义的信息内容单元，自然更适用于规范领域，如图片描述任务。在本文中，我们开发了DEPID，一种新的依赖性为基础的方法来计算PID，其版本DEPID-R，能够排除重复的想法-AD语音的特征特性。我们进行了第一次比较自动提取的PID和SID在诊断分类任务的两个不同的AD数据集，涵盖封闭主题和自由回忆域。虽然SID在标准数据集上表现更好，但添加PID会导致小但显着的改善（+1.7 F分数）。在自由主题数据集上，PID的表现优于SID（F分数为77.6 vs 72.3），但添加从自动SID底层的词嵌入聚类中获得的特征大大增加了结果，导致F分数为84.8。
英文摘要：
Idea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an increased risk of developing Alzheimer's disease (AD) (Snowdon et al., 1996; Engelman et al., 2010). ID has been used in two different versions: propositional idea density (PID) counts the expressed ideas and can be applied to any text while semantic idea density (SID) counts pre-defined information content units and is naturally more applicable to normative domains, such as picture description tasks. In this paper, we develop DEPID, a novel dependency-based method for computing PID, and its version DEPID-R that enables to exclude repeating ideas---a feature characteristic to AD speech. We conduct the first comparison of automatically extracted PID and SID in the diagnostic classification task on two different AD datasets covering both closed-topic and free-recall domains. While SID performs better on the normative dataset, adding PID leads to a small but significant improvement (+1.7 F-score). On the free-topic dataset, PID performs better than SID as expected (77.6 vs 72.3 in F-score) but adding the features derived from the word embedding clustering underlying the automatic SID increases the results considerably, leading to an F-score of 84.8.
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