On the importance of normative data in speech-based assessment
This work addresses data scarcity in medical AI for Alzheimer's disease diagnosis, though it is incremental as it builds on existing methods and datasets.
The authors tackled the problem of sparse Alzheimer's disease (AD) data limiting model generalizability by augmenting the DementiaBank dataset with two normative speech datasets, achieving state-of-the-art results in binary classification of AD.
Data sets for identifying Alzheimer's disease (AD) are often relatively sparse, which limits their ability to train generalizable models. Here, we augment such a data set, DementiaBank, with each of two normative data sets, the Wisconsin Longitudinal Study and Talk2Me, each of which employs a speech-based picture-description assessment. Through minority class oversampling with ADASYN, we outperform state-of-the-art results in binary classification of people with and without AD in DementiaBank. This work highlights the effectiveness of combining sparse and difficult-to-acquire patient data with relatively large and easily accessible normative datasets.