المؤلفون: Caroline Kuhn, Ebru Baykara, Julia Karbach, Johannes Tröger, Nicklas Linz

مصطلحات موضوعية: Engineering drawing, Computer science, Trail Making Test

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::be770f364650305bd3278f88224985a9Test
https://doi.org/10.1111/ejn.15541/v2/response1Test

المؤلفون: Ebru Baykara, Nicklas Linz, Johannes Tröger, Caroline Kuhn, Julia Karbach

المصدر: The European journal of neuroscienceREFERENCES. 55(2)

مصطلحات موضوعية: Adult, Elementary cognitive task, Modality (human–computer interaction), Modalities, Trail Making Test, medicine.diagnostic_test, Working memory, Computer science, General Neuroscience, Neuropsychology, Flexibility (personality), Neuropsychological Tests, Memory, Short-Term, Human–computer interaction, medicine, Humans, Neuropsychological assessment

الوصف: Using digital technology for neuropsychological assessment is gaining popularity in both clinical and research settings. Digital neuropsychology offers many benefits over the traditional paper-pencil assessments; however, their comparability requires further validation. The aim of this study was to compare a digital, tablet-based Trail Making Test to the standard paper version. In a within-subject design, 108 healthy adults completed both digital and paper Trail Making Test in a counterbalanced order. Each participant also performed other tasks measuring core executive abilities (inhibition, working memory, and flexibility) on the tablet. Our findings indicated that the Trail Making Test performance on the two different modalities correlated significantly. Furthermore, correlations of Trail Making Test performance with other cognitive tasks revealed that digital Trail Making Test is comparable with the paper version. However, the modality had a significant effect on Trail Making Test performance; that is, participants were generally faster on the digital platform. Taken together, our findings suggest that with new normative data, traditional Trail Making Test can be adapted successfully to a digital platform.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d09a584f62491cbb633fd84ea814d78fTest
https://pubmed.ncbi.nlm.nih.gov/34811827Test

المؤلفون: Nicklas Linz, Philipp Müller, Insa Kröger, Frans R.J. Verhey, Inez H.G.B. Ramakers, Radia Zeghari, Alexandra König, Johannes Tröger, Hali Lindsay

المصدر: RANLP

مصطلحات موضوعية: Computer science, business.industry, computer.software_genre, Variety (linguistics), language.human_language, Test (assessment), Task (project management), German, language, Verbal fluency test, Screening tool, Artificial intelligence, Cognitive impairment, business, computer, Word (computer architecture), Natural language processing

الوصف: The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6f46b5be971094a6d07989b35a6bbbb1Test
https://doi.org/10.26615/978-954-452-072-4_095Test

المؤلفون: Radia Zeghari, Robert Philippe, Valeria Manera, Nicklas Linz, Rachid Guerchouche, Alexandra König

المصدر: Alzheimer's & Dementia. 15

مصطلحات موضوعية: Telemedicine, Multimedia, Epidemiology, Computer science, Health Policy, computer.software_genre, Cognitive test, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Automatic speech, Neurology (clinical), Geriatrics and Gerontology, Rural area, computer

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::b66d189ac1e50ce299187f6121fb3e5aTest
https://doi.org/10.1016/j.jalz.2019.09.064Test

المؤلفون: Dimitrios Kokkinakis, Nicklas Linz, Philippe Robert, Frank Rudzicz, Jan Alexandersson, Bai Li, Alexandra König, Kathleen C. Fraser, Kristina Lundholm Fors

المصدر: NAACL-HLT (1)

مصطلحات موضوعية: Domain adaptation, Training set, business.industry, Computer science, computer.software_genre, medicine.disease, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Dementia, Language modelling, Language model, Artificial intelligence, 0305 other medical science, business, Set (psychology), computer, 030217 neurology & neurosurgery, Natural language processing

الوصف: 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.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f36e527a739665c9382b8af3ae241d45Test
https://doi.org/10.18653/v1/n19-1367Test

المؤلفون: Hali Lindsay, Alexandra König, Nicklas Linz, Kathleen C. Fraser

المساهمون: National Research Council of Canada (NRC), Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

المصدر: Computational Linguistics and Clinical Psychology : From Keyboard to Clinic
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology, Jun 2019, Minneapolis, Minnesota, United States. pp.55-61, ⟨10.18653/v1/W19-3007⟩

مصطلحات موضوعية: Data sharing, [SCCO]Cognitive science, Annotation, Research groups, Computer science, Position paper, Transcription (software), Data science

الوصف: Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.
Sixth Workshop on Computational Linguistics and Clinical Psychology, June 6, 2019, Minneapolis, Minnesota, United States

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eccc014d914a939893fc6cb7d053fc4Test
https://doi.org/10.18653/v1/w19-3007Test

المؤلفون: Jan Alexandersson, Philippe Robert, Alexandra König, Nicklas Linz, Johannes Tröger

المصدر: PervasiveHealth

مصطلحات موضوعية: Computer science, business.industry, Word error rate, computer.software_genre, medicine.disease, 3. Good health, 03 medical and health sciences, Statistical classification, 0302 clinical medicine, Resource (project management), Pattern recognition (psychology), Classifier (linguistics), medicine, Dementia, Verbal fluency test, 030212 general & internal medicine, Artificial intelligence, Cognitive decline, business, computer, 030217 neurology & neurosurgery, Natural language processing

الوصف: Dementia has a large economic impact on our society as cost-effective population-wide screening for early signs of dementia is still an unsolved medical supply resource problem. A solution should be fast, require a minimum of external material, and automatically indicate potential persons at risk of cognitive decline. Despite encouraging results, leveraging pervasive sensing technologies for automatic dementia screening, there are still two main issues: significant hardware costs or installation efforts and the challenge of effective pattern recognition. Conversely, automatic speech recognition (ASR) and speech analysis have reached sufficient maturity and allow for low-tech remote telephone-based screening scenarios. Therefore, we examine the technologic feasibility of automatically assessing a neuropsychological test---Semantic Verbal Fluency (SVF)--via a telephone-based solution. We investigate its suitability for inclusion into an automated dementia frontline screening and global risk assessment, based on concise telephone-sampled speech, ASR and machine learning classification. Results are encouraging showing an area under the curve (AUC) of 0.85. We observe a relatively low word error rate of 33% despite phone-quality speech samples and a mean age of 77 years of the participants. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::db927cef3a693060a6ddc3765aed10acTest
https://doi.org/10.1145/3240925.3240943Test

المؤلفون: P. Robert, Alexandra König, Nicklas Linz, Johannes Tröger, Jan Alexandersson

المصدر: French Journal of Psychiatry. 1:S76

مصطلحات موضوعية: Computer science, business.industry, Word error rate, Cognition, computer.software_genre, Task (project management), Classifier (linguistics), Semantic memory, Verbal fluency test, Artificial intelligence, Cognitive decline, business, computer, Neurocognitive, Natural language processing

الوصف: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI) and seem to be highly sensitive to cognitive changes [1]. In this task, participants name as many items of a semantic category under a time constraint. SVF can be considered as a multifactorial task, comprising both semantic memory retrieval and executive control processes. However, clinicians measure task performance manually only by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic speech analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Furthermore, we examined the technologic feasibility of automatically assessing the SVF task – via a telephone-based solution for potential remote frontline prescreening of cognitive impairments [2]. We will present SVF data which was collected from 95 older people with MCI (n=47), Alzheimer’s or related dementias (ADRD; n=24) and healthy controls (HC; n=24). Speech was recorded through a mobile tablet device using the built-in microphone. All data was annotated manually and automatically with the named words, clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI and ADRD. Afterwards to simulate telephone conditions, the recordings were downsampled to a lower sampling rate. Automatically extracted clusters and switches were highly correlated (r=0.9) with manually established values, and performed as well on the classification task separating healthy controls from persons with Alzheimer’s (AUC=0.939) and MCI (AUC=0.758) [3]. The downsampled quality obtained similar encouraging results. We observe a relatively low word error rate of 33% despite phone-quality speech samples. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system and that it is possible to automate fine-grained analyses of this task for the assessment of cognitive decline.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f570a363489a16ac7a6174acf5b1a066Test
https://doi.org/10.1016/s2590-2415Test(19)30183-7