Exploring neural models for predicting dementia from language

Kong, Weirui; Jang, Hyeju; Carenini, Giuseppe; Field, Thalia S.

doi:10.1016/j.csl.2020.101181

Cited by 15 publications

(14 citation statements)

References 39 publications

(39 reference statements)

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“…There has been substantial work using spontaneous speech samples and manual transcriptions present in the DementiaBank dataset [31]. Some of the highest reported scores for AD classification are 0.87, 0.85, 0.82, 0.80, 0.79, 0.64, and 0.63 [24,25,13,26,27,28,29]. Many of these previous results were obtained on datasets with variable subject dependencies.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The best of their solutions reached 86.5% leave-one-out cross-validation (LOOCV) accuracy with 38 subjects. Works based on data extracted from DementiaBank have reported scores of around 0.87, 0.85, 0.82, 0.80, 0.79, 0.64, and 0.62 [24,25,13,26,27,28,29] for AD classification. Study [30] used speech related features to get a mean absolute error (MAE) of 3.83 for MMSE scores with longitudinal data derived from DementiaBank.…”

Section: Related Workmentioning

confidence: 99%

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Multimodal Inductive Transfer Learning for Detection of Alzheimer’s Dementia and its Severity

Sarawgi

Zulfikar

Soliman³

et al. 2020

Interspeech 2020

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Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/ wazeerzulfikar/alzheimers-dementia

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multimodal Inductive Transfer Learning for Detection of Alzheimer’s Dementia and its Severity

Sarawgi

Zulfikar

Soliman³

et al. 2020

Interspeech 2020

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“…For the picture description task, we extracted a comprehensive set of language features following (Fraser et al, 2016 ) as in our previous work (Field et al, 2017 ; Kong et al, 2019 ; Barral et al, 2020 ). These features comprise text features and acoustic features.…”

Section: Methodsmentioning

confidence: 99%

“…More recently, deep learning approaches have further improved classification performance. Our group used a hierarchical attention Recurrent Neural Network (RNN) model incorporating both raw text and patient’s age, leading to 86.9% accuracy using DementiaBank data (Kong et al, 2019 ). Karlekar et al ( 2018 ) achieved 91% accuracy using a Convoluted Neural Network (CNN)-RNN model trained on part-of-speech-tagged utterances.…”

Section: Related Workmentioning

confidence: 99%

“…In the picture description task, participants describe the Boston Cookie Theft picture from the Boston Diagnostic Aphasia Examination (Goodglass and Edith, 1972; Table 2), a widely used and validated method for spontaneous speech assessment in a variety of clinical contexts, including Alzheimer's disease (Cummings, 2019). It has been used in prior work for AD prediction using language (Fraser et al, 2016;Karlekar et al, 2018;Kong et al, 2019).…”

Section: Language and Eye-movement Assessmentmentioning

confidence: 99%

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Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data

Jang

Soroski

Rizzo

et al. 2021

Front. Hum. Neurosci.

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Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.

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AI‐based assessments of speech and language impairments in dementia

Parsapoor

2023

Alzheimer's & Dementia

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Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI‐powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.

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Exploring neural models for predicting dementia from language

Cited by 15 publications

References 39 publications

Multimodal Inductive Transfer Learning for Detection of Alzheimer’s Dementia and its Severity

Multimodal Inductive Transfer Learning for Detection of Alzheimer’s Dementia and its Severity

Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data

AI‐based assessments of speech and language impairments in dementia

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