Kristina Lundholm Fors


2019

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Temporal Analysis of the Semantic Verbal Fluency Task in Persons with Subjective and Mild Cognitive Impairment
Nicklas Linz | Kristina Lundholm Fors | Hali Lindsay | Marie Eckerström | Jan Alexandersson | Dimitrios Kokkinakis
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person’s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.

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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling
Kathleen C. Fraser | Nicklas Linz | Bai Li | Kristina Lundholm Fors | Frank Rudzicz | Alexandra König | Jan Alexandersson | Philippe Robert | Dimitrios Kokkinakis
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.

2018

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A Swedish Cookie-Theft Corpus
Dimitrios Kokkinakis | Kristina Lundholm Fors | Kathleen Fraser | Arto Nordlund
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Data Collection from Persons with Mild Forms of Cognitive Impairment and Healthy Controls - Infrastructure for Classification and Prediction of Dementia
Dimitrios Kokkinakis | Kristina Lundholm Fors | Eva Björkner | Arto Nordlund
Proceedings of the 21st Nordic Conference on Computational Linguistics

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An analysis of eye-movements during reading for the detection of mild cognitive impairment
Kathleen C. Fraser | Kristina Lundholm Fors | Dimitrios Kokkinakis | Arto Nordlund
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy.

2016

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Data Resource Acquisition from People at Various Stages of Cognitive Decline – Design and Exploration Considerations
Dimitrios Kokkinakis | Kristina Lundholm Fors | Arto Nordlund
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis