المؤلفون: Marie Eckerström, Jan Alexandersson, Kristina Lundholm Fors, Nicklas Linz, Dimitrios Kokkinakis, Hali Lindsay

المصدر: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology.

مصطلحات موضوعية: Vocabulary, medicine.medical_specialty, medicine.diagnostic_test, media_common.quotation_subject, Word count, Neuropsychology, Audiology, Task (project management), Interval (music), medicine, Verbal fluency test, Neuropsychological assessment, Psychology, Word (group theory), media_common

الوصف: 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.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0c8dbc3f3e0001bd0c156473ff5c3548Test
https://doi.org/10.18653/v1/w19-3012Test

المؤلفون: P. Robert, Johannes Tröger, Nicklas Linz, Alexandra König, Radia Zeghari

المصدر: French Journal of Psychiatry. 1:S50

الوصف: Aujourd’hui, il y a un besoin croissant d’harmonisation et d’innovation dans les evaluations neuropsychologiques. Etant donne que les outils de diagnostic actuels sont parfois invasifs (scan), couteux (imagerie) et longues (evaluation neuropsychologique classique), de nouvelles methodes ecologiquement valides et sensibles sont necessaires pour ameliorer l’accessibilite en tant que depistage de premiere ligne dans la population fragile. Les technologies numeriques intelligentes peuvent servir d’outils supplementaires non invasifs et/ou rentables, permettant d’identifier les sujets au stade preclinique ou aux premiers stades cliniques par exemple de la demence ou outres troubles psychiatriques qui pourraient convenir a un essai clinique, ainsi que de surveiller plus continuellement la trajectoire des maladies. De plus, des mesures plus normalisees pourraient entrainer une reduction des erreurs dues aux annotations humaines, ce qui faciliterait de meilleures comparaisons de donnees entre les sites cliniques. La mise en œuvre de ces nouvelles methodes de mesure pourrait faciliter le diagnostic precoce et des strategies de prevention et de traitement des troubles cognitifs et psychiatriques potentiellement plus efficaces. Toutefois, avant de les appliquer a la pratique et aux essais cliniques, ces outils devraient etre examines dans les cohortes importantes en cours. En s’appuyant sur les procedures existantes, des tentatives ont ete faites pour ameliorer et reinventer les metriques classiques en utilisant l’intelligence artificielle – analyse et tests informatises, jeux serieux, capteurs portables, analyse de la parole et de l’image ou crayons intelligents. Alexandra Konig, chercheuse/psychologue a l’INRIA et au CoBTeK lab, presentera l’application sur iPad « Δelta » permettant aux cliniciens de faire passer et d’analyser automatiquement des tests neuropsychologiques classiques meme a distance a l’aide de l’intelligence artificielle (IA) et l’analyse automatisee du discours. Un exemple d’une analyse psycholinguistique informatisee d’une entrevue clinique sera egalement presenter.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0c678c7daac1083cf04853a4298eeba3Test
https://doi.org/10.1016/j.fjpsy.2019.10.143Test

المؤلفون: Gabriel Robert, Marco Lorenzi, Radia Zeghari, P. Robert, Nicholas Ayache, Nicklas Linz, L. Domain, Valeria Manera, Alexandra König, C. Abi Nader

المصدر: French Journal of Psychiatry. 1:S49

الوصف: Aujourd’hui, il y a un besoin croissant d’harmonisation et d’innovation dans les evaluations cognitives et comportementales. Les outils actuels sont parfois trop invasifs, couteux ou demande des temps de realisations trop important dans le cadre d’une simple consultation. De ce faite de nouvelles methodes ecologiquement valides et sensibles pourraient etre utiles pour ameliorer l’accessibilite en tant que depistage de premiere ligne dans la population souffrant de troubles neuropsychiatriques. Les technologies de l’information et de la communication (TIC) sont des solutions non invasives et qui ont montrees une utilite pour identifier les sujets aux premiers stades cliniques des maladies neurodegeneratives [1] , [2] . Les recherches actuelles s’orientent sur l’interet des ICT a un stade pre clinique, comme marqueur d’evolution, au cours des essais therapeutiques et dans les troubles psychiatriques [3] , [4] . Cette session a pour objectif d’illustrer les scenario d’utilisation d’outils numeriques novateurs, qui pourraient etre utilises pour le depistage a grande echelle et pour le suivi des patients dans les essais cliniques. Lea Domain, interne de psychiatrie au centre hospitalier Guillaume-Regnier de Rennes presentera les resultats preliminaires de l’etude DEFLUENCE. Cette etude a pour objectif de determiner si les alterations qualitatives aux tests de fluences verbales mesurees de facon automatisee peuvent constituer un bio marqueur pronostic de l’evolution de la depression. Alexandra Konig, neuropsychologue et chercheuse au laboratoire CoBTeK presentera l’application Δelta sur tablette qui permet aux cliniciens de faire passer et d’analyser automatiquement des tests neuropsychologiques classiques meme a distance a l’aide de l’intelligence artificielle (IA), de l’analyse automatisee de l’expression faciale et de la voix. Un exemple d’une analyse psycholinguistique informatisee d’une entrevue clinique sera egalement presentee. Clement Abi Nader, doctorant dans l’equipe Epione INRIA presentera les travaux portant sur la modelisation de l’evolution de la maladie d’Alzheimer a partir de donnees cliniques longitudinales acquises. Cette approche consiste a developper des algorithmes integrant des donnees heterogenes (imagerie, donnees biologiques, capteurs, donnees cliniques).

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::9e4343459364a9c79f4dcbee410d19a9Test
https://doi.org/10.1016/j.fjpsy.2019.10.141Test

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

المساهمون: Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Cognition Behaviour Technology (CobTek), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), 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), Université Nice Sophia Antipolis (1965 - 2019) (UNS)

المصدر: 2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18
2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Jul 2018, Stockholm Sweden
HAL

مصطلحات موضوعية: [SCCO]Cognitive science, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.PSYC]Cognitive science/Psychology, otorhinolaryngologic diseases, [SCCO.LING]Cognitive science/Linguistics

الوصف: International audience; Apathy is a frequent neuropsychiatric syndrome in people with dementia. It leads to diminished motivation for physical, cognitive and emotional activity. Apathy is highly underdiagnosed since its criteria have been only recently established and rely heavily on the subjective evaluation of human observers. In this paper we analyse speech samples from demented people with and without apathy. Speech was provoked by asking patients two emotional questions. Acoustic features were extracted and used in a classification task. The resulting models show performances of AUC = 0:71 and AUC = 0:63. This is a decent first step into the direction of automatic detection of apathy from speech. Usefulness of stimuli to elicit free speech is found to depend on patients gender.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::367b2531be9b743ab3de3d1620ca0936Test
https://hal.inria.fr/hal-01850436/documentTest

المؤلفون: Auriane Gros, Alexandra König, Philippe Robert, Aurore Rainouard, Jan Alexandersson, Johannes Tröger, Nicklas Linz

المصدر: Alzheimer's & Dementia. 14

مصطلحات موضوعية: medicine.medical_specialty, Epidemiology, Health Policy, Audiology, medicine.disease, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Automatic speech, medicine, Dementia, Neurology (clinical), Geriatrics and Gerontology, Psychology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::222bd9a5248326c20dfc1848ee495ee7Test
https://doi.org/10.1016/j.jalz.2018.06.2482Test

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

المساهمون: Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Cognition Behaviour Technology (CobTek), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), 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), University of Edinburgh, Université Nice Sophia Antipolis (... - 2019) (UNS)

المصدر: ICDM 2017-IEEE International Conference on Data Mining, Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living
ICDM 2017-IEEE International Conference on Data Mining, Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living, Nov 2017, New Orleans, United States. pp.719-728, ⟨10.1109/ICDMW.2017.100⟩
ICDM Workshops
Linz, N, Troeger, J, Alexandersson, J, Koenig, A, Robert, P & Wolters, M 2017, Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance . in First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living . Institute of Electrical and Electronics Engineers (IEEE), 2017 IEEE International Conference on Data Mining Workshops, New Orleans, Louisiana, United States, 18/11/17 . https://doi.org/10.1109/ICDMW.2017.100Test

مصطلحات موضوعية: Mini–Mental State Examination, medicine.diagnostic_test, Clinical Dementia Rating, 05 social sciences, Regression analysis, Cognition, medicine.disease, 050105 experimental psychology, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Cognitive test, 03 medical and health sciences, 0302 clinical medicine, mental disorders, medicine, Verbal fluency test, Dementia, 0501 psychology and cognitive sciences, Set (psychology), Psychology, 030217 neurology & neurosurgery, Clinical psychology

الوصف: International audience; The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea4a62da9cbde6bab99072d432da428cTest
https://inria.hal.science/hal-01672590Test

المؤلفون: Jan Alexandersson, Nicklas Linz, Philippe Robert, Kathleen C. Fraser, Francois Bremond, Alexandra König, Maria Wolters, Liam D. Kaufman, Johannes Tröger, Frank Rudzicz

المصدر: Alzheimer's & Dementia. 13

مصطلحات موضوعية: 030214 geriatrics, Basis (linear algebra), Epidemiology, Health Policy, Early detection, Cognition, medicine.disease, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, medicine, Dementia, Neurology (clinical), Geriatrics and Gerontology, Psychology, 030217 neurology & neurosurgery, Cross linguistic, Cognitive psychology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::fe28c0202ff15f467f8bb8074f5d32f5Test
https://doi.org/10.1016/j.jalz.2017.06.311Test

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

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

الإتاحة: https://doi.org/10.1111/ejn.15541/v2/response1Test