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Predicting MCI Stat...
Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers
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Fraser, Kathleen, 1984 (författare)
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- Lundholm Fors, Kristina, 1977 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för svenska språket,Department of Swedish
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- Eckerström, Marie, 1981 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
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- Öhman, Fredrik (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
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- Kokkinakis, Dimitrios, 1965 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för svenska språket,Centrum för åldrande och hälsa (AgeCap),Department of Swedish,Centre for Ageing and Health (Agecap)
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(creator_code:org_t)
- 2019-08-02
- 2019
- Engelska.
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Ingår i: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365. ; 11:205
- Relaterad länk:
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https://doi.org/10.3...
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https://gup.ub.gu.se...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the "early fusion" approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (rho = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
- HUMANIORA -- Språk och litteratur -- Jämförande språkvetenskap och allmän lingvistik (hsv//swe)
- HUMANITIES -- Languages and Literature -- General Language Studies and Linguistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Nyckelord
- mild cognitive impairment
- language
- speech
- eye-tracking
- machine learning
- multimodal
- early
- mild cognitive impairment
- alzheimers-disease
- spontaneous speech
- picture description
- memory
- integration
- decline
- identification
- comprehension
- recognition
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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