BibTeX

@inProceedings{kokkinakis-lundholmfors-2020-digital-295582,
	title        = {Digital Neuropsychological Tests and Biomarkers: Resources for NLP and AI Exploration in the Neuropsychological Domain},
	abstract     = {Non-invasive, time and cost-effective, easy-to-measure techniques for the early diagnosis or monitoring the progression of brain and mental disorders are at the forefront of recent research in this field. Natural Language Processing and Artificial Intelligence can play an important role in supporting and enhancing data driven approaches to improve the accuracy of prediction and classification. However, large datasets of e.g. recorded speech in the domain of cognitive health are limited. To improve the performance of existing models we need to train them on larger datasets, which could raise the accuracy of clinical diagnosis, and contribute to the detection of early signs at scale. In this paper, we outline our ongoing work to collect such data from a large population in order to support and conduct future research for modelling speech and language features in a cross-disciplinary manner. The final goal is to explore and combine linguistic with multimodal biomarkers from the same population and compare hybrid models that could increase the predictive accuracy of the algorithms that operate on them.},
	booktitle    = {CLARIN Annual Conference 2020 in Virtual Form},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina},
	year         = {2020},
}