Name
Abstract | ||
|---|---|---|
Data sets for identifying Alzheimeru0027s 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Data set,Longitudinal study,Binary classification,Oversampling,Normative,Computer science,Artificial intelligence,Natural language processing,Machine learning |
DocType | Volume | Citations |
Journal | abs/1712.00069 | 3 |
PageRank | References | Authors |
0.49 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeinab Noorian | 1 | 3 | 0.49 |
| Chloé Pou-Prom | 2 | 3 | 1.17 |
| Frank Rudzicz | 3 | 231 | 44.82 |