Detecting cognitive impairments by agreeing on interpretations of linguistic features
This work addresses the challenge of expensive clinical data acquisition and burdensome feature engineering for detecting cognitive impairments, offering a novel method to enhance detection accuracy.
The paper tackles the problem of detecting cognitive impairments by proposing Consensus Networks (CNs), a framework that improves classification accuracy by having neural networks learn low-dimensional representations from divided linguistic feature modalities that agree with each other, resulting in models that significantly outperform traditional classifiers using 413 linguistic features.
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.