Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias

Yancheva, Maria; Fraser, Kathleen; Rudzicz, Frank

doi:10.18653/v1/w15-5123

Cited by 57 publications

(66 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Automated discourse analysis tools based on Natural Language Processing (NLP) resources and tools aiming at the diagnosis of language-impairing dementias via machine learning methods are already available for the English language (Fraser et al, 2015b;Yancheva et al, 2015;Roark et al, 2011) and also for Brazilian Portuguese (BP) (Aluísio et al, 2016). The latter study used a publicly available tool, Coh-Metrix-Dementia 1 , to extract 73 textual metrics of narrative transcripts, comprising several levels of linguistic analysis from word counts to semantics and discourse.…”

Section: Introductionmentioning

confidence: 99%

Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

Treviso

Shulby

Aluísio

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of languageimpairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech, as well as, normal, prepared speech, and presents better results for healthy elderly (CTL) (F 1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F 1 = 0.70) than the Conditional Random Fields method (F 1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.

show abstract

Section: Introductionmentioning

confidence: 99%

Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

Treviso

Shulby

Aluísio

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

show abstract

“…• Following the convention of speech processing literatures (Zhou et al, 2016;Yancheva et al, 2015;Zhao et al, 2014), we compute Mel-scaled cepstral coefficients (MFCCs) containing the amount of energy in 12 different frequency intervals for each time frame of 40 milliseconds, as well as their first-and second-order derivatives. We calculate the mean, variance, kurtosis, and skewness of the MFCCs and include them as features.…”

Section: Appendix Linguistic Featuresmentioning

confidence: 99%

“…For example, Fraser et al (2015) achieved up to 82% accuracy on Demen-tiaBank 1 , the largest publicly available dataset on detecting cognitive impairments from speech, and Weissenbacher et al (2016) achieved up to 86% accuracy on a corpus of 500 subjects. Yancheva et al (2015) estimated Mini-Mental State Estimation scores (MMSEs), describing the cognitive status and characterizing the extent of cognitive impairment.…”

Section: Introductionmentioning

confidence: 99%

Detecting cognitive impairments by agreeing on interpretations of linguistic features

Zhu

Novikova

Rudzicz

2019

Proceedings of the 2019 Conference of the North

Self Cite

View full text Add to dashboard Cite

Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and handcrafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively.In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.

show abstract

“…Whilst differentiating between healthy participants and those with AD was successful, the classifier was not capable of categorising the healthy group from the AACD patients. [15,16] have used the DementiaBank corpus (containing speech of patients with AD, vascular dementia, MCI and healthy controls describing the 'Cookie Theft' picture) to predict changes in patients' Mini Mental State Examination (MMSE) scores over time. The researchers extracted a wide range of features (477 lexico-syntactic, acoustic, and semantic) and selected the 40 most informative, reporting an accuracy of over 92% in terms of the distinction of AD patients from healthy controls.…”

Section: Automatic Detection Of Dementiamentioning

confidence: 99%

An Avatar-Based System for Identifying Individuals Likely to Develop Dementia

et al. 2017

View full text Add to dashboard Cite

This paper presents work on developing an automatic dementia screening test based on patients' ability to interact and communicate -a highly cognitively demanding process where early signs of dementia can often be detected. Such a test would help general practitioners, with no specialist knowledge, make better diagnostic decisions as current tests lack specificity and sensitivity. We investigate the feasibility of basing the test on conversations between a 'talking head' (avatar) and a patient and we present a system for analysing such conversations for signs of dementia in the patient's speech and language. Previously we proposed a semi-automatic system that transcribed conversations between patients and neurologists and extracted conversation analysis style features in order to differentiate between patients with progressive neurodegenerative dementia (ND) and functional memory disorders (FMD). Determining who talks when in the conversations was performed manually. In this study, we investigate a fully automatic system including speaker diarisation, and the use of additional acoustic and lexical features. Initial results from a pilot study are presented which shows that the avatar conversations can successfully classify ND/FMD with around 91% accuracy, which is in line with previous results for conversations that were led by a neurologist.

show abstract

Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias

Cited by 57 publications

References 24 publications

Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

Detecting cognitive impairments by agreeing on interpretations of linguistic features

An Avatar-Based System for Identifying Individuals Likely to Develop Dementia

Contact Info

Product

Resources

About