Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

König, Alexandra; Satt, Aharon; Sorin, Alexander; Hoory, Ron; Toledo-Ronen, Orith; Derreumaux, Alexandre; Manera, Valéria; Verhey, Frans R.J.; Aalten, Pauline; Robert, P; David, Renaud

doi:10.1016/j.dadm.2014.11.012

Cited by 266 publications

(259 citation statements)

References 47 publications

(68 reference statements)

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“…In addition, ML has also been applied to the diagnosis of mental health conditions with similar symptomatology, for example differentiation of autism spectrum disorders and epilepsy using EEG data [21]. Research has also investigated the application of ML techniques to sensor, speech and video data to improve diagnosis of Alzheimer's disease [23], schizophrenia [24], and suicide ideation [25], achieving high accuracy. Finally, ML with wearable sensor data from actigraph monitors, has been demonstrated to differentiate between children with ADHD and bipolar disorder [22].…”

Section: Detection and Diagnosismentioning

confidence: 99%

“…Further research is also required to ensure that the techniques proposed in a research context can be translated into diagnosis options for the public. Mania NLP [128], NB [128], NN [128] Letters [128] Mild Cognitive Impairment BN [27,69], ensemble learning [27,69], Regression [30], RF [20], Similarity Discriminative Dictionary Learning (SCDDL) algorithm [41], SVM [23] Imaging [20,27,30,41,69], Audio [23] Obsessive Compulsive Disorder NN [129], kNN [129], NB [129], Searchlight Based Feature Extraction (SBFE) [130], SLR algorithm [131], L1-SCCA algorithm [131], SVM [129,132] Imaging [129][130][131][132] Parkinson's Disease SVM [38], RF [38], DT [38], Regression [38] Clinical Assessment [38] Prognosis, Treatment and Support…”

Section: Detection and Diagnosismentioning

confidence: 99%

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Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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Section: Detection and Diagnosismentioning

confidence: 99%

Section: Detection and Diagnosismentioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

View full text Add to dashboard Cite

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“…Key partnerships refers to the network of suppliers useful to improve the BM. Literature on CI suggests that some key partnership for the BM could be: (a) government; (b) research center/university; (c) local regional community (Kapadia et al 2015); (d) private organizations (König et al 2015); (e) networks between the providers of digital solutions and healthcare organizations.…”

Section: General Business Modelmentioning

confidence: 99%

A Business Model for Digital Healthcare Environments: An Organic Approach and a Use Case for Handling Cognitive Impairment

Pistorio

Locatelli

Cirilli³

et al. 2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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Abstract:Ageing has significant impacts on the organization of healthcare systems and on social inclusion-especially for elderly people affected by Cognitive Impairment (CI). These people are significantly exposed to undeniable risks that can affect their health and wellbeing (falling, malnutrition, hygiene issues, etc.) -especially when living alone. This paper defines a Business Model (BM) allowing independent living for elderly people affected by CI. This BM include: (i) an up-to-date, modular, flexible and scalable organizational model describing the activities to be accomplished by regulators and service suppliers; and (ii) a digital platform based on innovative and easy-to-replicate information and communication technologies. The organic approach to the development of the BM is then focused in an Italian use case as a part of "DECI", a "Horizon 2020" project with four pilot projects in Israel, Italy, Spain and Sweden.

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“…Thus, recent studies using automated or semiautomated identification have relied on different linguistic criteria such a lexical clues (Bucks et al, 2010; Asgari, Kaye and Dogde, 2017), syntactic complexity (Roark, Mitchel and Hollingshead, 2007), discourse phenomena (Habash, 2011) and even prosodic elements found in narrative language samples (Köning, et al, 2015). Despite these efforts, there are still limitations as the vast majority of researches have focused solely on English speaking populations.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

Taboada¹,

Cunha²,

Maziero³

et al. 2017

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ii Preface Rhetorical Structure Theory explains text coherence through relations held between parts of text, both in macro and microstructure . RST is both a theory of discourse and a useful tool in various applications. In Linguistics, and as a theory of discourse, RST is a framework for the analysis of texts. It accounts for text coherence by postulating relations among units of discourse, which join together in a recursive fashion. It has provided important insights into text coherence, clause combining and discourse organization in language, across multiple languages. In Computational Linguistics, RST has proven useful in applications such as sentiment analysis and machine translation. A detailed description, including multiple references, is available from the RST web site: http://www.sfu.ca/rst/.The 2017 Workshop on "RST and Related Formalisms" follows a series of events organized biannually first in Brazil (2007Brazil ( , 2009Brazil ( , 2011Brazil ( , 2013 as part of Brazilian NLP conferences, and then in Spain in 2015, as part of the Spanish NLP conference (https://sites.google.com/site/ workshoprst2015/). The workshops have brought together the international community of researchers working in RST.The 2017 Workshop was envisioned as a broader event, drawing program committee members and participants not only from RST, but from the wider field of discourse parsing and coherence relations, in RST and in related theories and approximations (SDRT, PDTB, CCR). We received 11 submissions, and each one was reviewed by two members of the Program Committee, according to the following criteria: originality; adequate theoretical support and methodology; quality of the analysis; clarity of presentation and relevant references. Of the 11 submissions, 10 were accepted. We also expanded the program with a demo session.We would like to thank the members of the Program Committee for their commitment to the workshop. We look forward to another instance of the workshop in the future, and to much more excellent RST-based research. REFERENCE AbstractDeliberation is an increasingly used concept in Argumentation Theory and Linguistic Analysis. But only recently research combined empirical and conceptual tool-boxes from these disciplines for the study of deliberative discourse. The aim of this article is to present a discursive analysis of deliberation as a genre using the relational discourse structure of texts. In particular, we want to see whether different features of deliberation genre map onto relational discourse structures of texts.To do so, authors analyze, in the framework of Rhetorical Structure Theory (RST), the relational discourse structure of a Basque-Spanish bilingual corpus of argumentative micro-texts written by citizens participating in a series of deliberative mini-publics. Results suggest that genre affects the relational discourse structures of texts and that we could analyze this effect in RST. Finally, we present, to our knowledge, the first annotated corpus-based genre analysis of the relational d...

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Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

Cited by 266 publications

References 47 publications

Machine learning in mental health: A systematic scoping review of methods and applications

Machine learning in mental health: A systematic scoping review of methods and applications

A Business Model for Digital Healthcare Environments: An Organic Approach and a Use Case for Handling Cognitive Impairment

Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

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