Language Characteristics supporting early Alzheimer's diagnosis trough machine leaning - a literature review

[img] Text
Language Characteristics supporting early Alzheimer's diagnosis trough machine leaning10121hiij02.pdf

Downloads (509kB)
Creators: Thaler, Fabian and Gewald, Heiko
Title: Language Characteristics supporting early Alzheimer's diagnosis trough machine leaning - a literature review
Item Type: Article
Projects: CROSS
Journal or Publication Title: Health informatics - An International Journal : HIIJ
Date: February 2021
Divisions: Informationsmanagement
Abstract: Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a promising instrument in this context. Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract this deficit and provide topic-related researchers with a better basis for decision-making, we present, based on a literature review, favourable speech characteristics for the appliance toward AD detection via ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and points out that the combined use of acoustic, linguistic, and demographic features positively influences recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic features
Forthcoming: No
Link eMedia: Download
Citation:

Thaler, Fabian and Gewald, Heiko (2021) Language Characteristics supporting early Alzheimer's diagnosis trough machine leaning - a literature review. Health informatics - An International Journal : HIIJ, 10 (1). ISSN 2319-2046

Actions (login required)

View Item View Item