Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia

Orimaye, Sylvester Olubolu; Wong, Jojo Sze-Meng; Wong, Chin Chin

doi:10.1371/journal.pone.0205636

Cited by 43 publications

(38 citation statements)

References 29 publications

(57 reference statements)

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“…At the same time, we believe our analysis uncovers the fact that no single underlying construct can characterize the complicated nature of MCI-AD [2,13]. As such, these multiple linguistic constructs could be used in a linguistic battery that captures essential linguistic biomarkers for identifying patterns of impaired speech that is specific to patients with MCI-AD [6,8,11,12,15]. Table 4 shows the underlying linguistic construct for the healthy control group.…”

Section: Underlying Linguistic Constructs With Exploratory Factor Anamentioning

confidence: 92%

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

et al. 2020

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There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.

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Section: Underlying Linguistic Constructs With Exploratory Factor Anamentioning

confidence: 92%

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

et al. 2020

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“…Previous research has shown the effectiveness of neural models trained on conversational transcripts at identifying useful features for dementia classification (Lyu, 2018;Karlekar et al, 2018;Olubolu Orimaye et al, 2018). Nevertheless, other information that has proven to be crucial to the task cannot be derived from interview transcripts themselves.…”

Section: Targeted Featuresmentioning

confidence: 99%

Enriching Neural Models with Targeted Features for Dementia Detection

Palo¹,

Parde²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Alzheimer's disease (AD) is an irreversible brain disease that can dramatically reduce quality of life, most commonly manifesting in older adults and eventually leading to the need for full-time care. Early detection is fundamental to slowing its progression; however, diagnosis can be expensive, time-consuming, and invasive. In this work we develop a neural model based on a CNN-LSTM architecture that learns to detect AD and related dementias using targeted and implicitly-learned features from conversational transcripts. Our approach establishes the new state of the art on the De-mentiaBank dataset, achieving an F1 score of 0.929 when classifying participants into AD and control groups.

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“…In recent years, deep neural network (DNN) has garnered clinical interests in cognitive diagnostic applications due to its advantages in efficient classification. Moreover, many existing methods have been proposed, where some of them [4]- [7] combine DNN with neuroimaging markers, while other methods [8]- [10] combine DNN with neuropsychological assessments. Jain et al [5] proposed a transfer learning approach for accurately classifying brain sMRI slices amongst 3 different classes: Alzheimer's disease (AD), cognitively normal(CN) and mild cognitive impairment (MCI).…”

Section: Introductionmentioning

confidence: 99%

“…They analyzed both a T1-weighted MRI scan and FDG-PET image data of 1242 subjects/patients. Orimaye et al [8] proposed a method that combined deep neural network and deep language models (D2NNLM) for classifying the disease. The experimental results showed that the model could accurately predict MCI and AD type dementia on a very sparse clinical language dataset.…”

Section: Introductionmentioning

confidence: 99%

Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning

Zhu

McGonigle

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. Results: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). Conclusion: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe). INDEX TERMS Dementia, information gain, deep neural network, machine learning.

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Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia

Cited by 43 publications

References 29 publications

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

Enriching Neural Models with Targeted Features for Dementia Detection

Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning

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