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: Pascale Lemoine, Philippe Robert, Vincent Bultingaire, Alexandra König, Nicklas Linz, Radia Zeghari, Rachid Guerchouche, Inez H.G.B. Ramakers

المصدر: Alzheimer's & Dementia. 16

مصطلحات موضوعية: 0303 health sciences, Telemedicine, Epidemiology, business.industry, Remote patient monitoring, Health Policy, medicine.disease, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, Medicine, Dementia, Neurology (clinical), Medical emergency, Geriatrics and Gerontology, business, 030217 neurology & neurosurgery, 030304 developmental biology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0cb638aff51460d2be335b03b85cdca4Test
https://doi.org/10.1002/alz.047345Test

المؤلفون: Hali Lindsay, Philippe Robert, Nicklas Linz, Julia Elbaum, Kevin Riviere, Alexandre Derreumaux, Roxane Fabre, Alexandra König

المساهمون: Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Spatio-Temporal Activity Recognition Systems (STARS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre Hospitalier Universitaire de Nice (CHU Nice), ki:elements [Saarbrücken], 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)

المصدر: Journal of Medical Internet Research, Vol 23, Iss 4, p e24191 (2021)
Journal of Medical Internet Research
Journal of Medical Internet Research, 2021, 23 (4), pp.e24191. ⟨10.2196/24191⟩
Journal of Medical Internet Research, JMIR Publications, 2021, 23 (4), pp.e24191. ⟨10.2196/24191⟩

مصطلحات موضوعية: Adult, Male, Health Personnel, speech, Computer applications to medicine. Medical informatics, 0206 medical engineering, R858-859.7, Pilot Projects, Health Informatics, 02 engineering and technology, Anxiety, Speech Acoustics, Voice analysis, phone monitoring, [SCCO]Cognitive science, 03 medical and health sciences, 0302 clinical medicine, Phone, Surveys and Questionnaires, Health care, medicine, Humans, Stress measures, Burnout, Professional, Pandemics, Original Paper, computer linguistics, SARS-CoV-2, business.industry, COVID-19, 020601 biomedical engineering, Telephone, 3. Good health, voice analysis, Female, Observational study, Public aspects of medicine, RA1-1270, medicine.symptom, business, Psychology, Psychosocial, 030217 neurology & neurosurgery, stress detection, Clinical psychology

الوصف: BackgroundDuring the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior.ObjectiveThis study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants’ speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks.MethodsSpeech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed.ResultsCertain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31).ConclusionsAutomatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2dca6c3a053b0e42769a679c9562ebeTest
https://doi.org/10.2196/24191Test

المؤلفون: Gabriel Robert, Alexandra König, Isabelle Corouge, Nicklas Linz, M. Guillery, Elise Bannier, L. Domain, Dominique Drapier

المصدر: French Journal of Psychiatry. 1:S49-S50

مصطلحات موضوعية: medicine.medical_specialty, medicine.diagnostic_test, business.industry, Cognition, Disease, Neuropsychological test, Audiology, medicine.disease, Correlation, medicine, Major depressive disorder, Verbal fluency test, Cluster analysis, business, Depression (differential diagnoses)

الوصف: Background Major depressive disorder (MDD) is a highly disabling disease of which is related to chronic course. Currently, there is no robust prognosis biomarker. Cognitive dysfunction associated with the disease has been shown to predict treatment outcome. However, its cerebral substrate is poorly known. Verbal fluency (VF), which are broadly used neuropsychological test, assess the production of words according to a required criterion within a constrained time frame. During this task, subjects produce words within subcategories or “clusters” and occasionally “switch” to other clusters. The measurement of the cluster size and number of switches are relevant cognitive markers. Deep learning-based computational analysis method have been shown to improve the reproducibility and scalability of this measurement. The main objective of the current study was to characterize the cortical thickness (CT) surface-based morphometry (SBM) correlates of the switching and clustering scores in patients with MDD using an artificial intelligence empowered speech analysis system. The secondary objective was to compare those cognitive scores with the ones of the control group. Method The study included 26 depressed women and 25 matched in age and education level healthy females’ controls. All subjects underwent VF tests and had a structural Magnetic Resonance Imaging (MRI). The clustering and switching scores were assessed using the computational analysis method introduced by Linz et al ( https://ki-elements.deTest ). SBM was performed using SPM and CAT12 toolbox. Results In the depressed group, we observed a positive correlation between the switching score in semantic VF test and the CT, in few cortical areas. The main significant correlation was found in the left lingual gyrus (P = 0.003). The switching scores were lower in the depressed group in each VF task (P = 0.019 and P = 0.007). There was no difference between the two groups concerning the cluster size. Conclusion Our study revealed the relationship existing between the number of switches produced by depressed subjects in a semantic VF task and the CT in prefrontal regions and in more posterior areas. These results possibly suggest that switching and clustering scores assessment in VF tasks, and its cerebral correlates, may constitute potential prognosis biomarkers for depression. Although much work is still required, the establishment of such biomarkers has substantial implications for reducing the burden of the disease.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6c46ec8be376898cbb0c7f35484c6ca1Test
https://doi.org/10.1016/j.fjpsy.2019.10.142Test

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: 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