NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia

Chiu, Pai-Yi; Tang, Haipeng; Wei, Cheng‐Yu; Zhang, Chaoyang; Hung, Guang-Uei; Zhou, Weihua

doi:10.1371/journal.pone.0213430

Cited by 41 publications

(54 citation statements)

References 31 publications

(52 reference statements)

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“…The data for the study consisted of samples of clinical and neuropsychological assessment obtained from 6701 patients. [11]. Along with the current scales such as CDR, MMSE, CASI, MoCA, we used a newly designed Informant-based questionnaire named HAICDDS which is applied in dementia registration in a health system with 9 regional hospitals in Taiwan.…”

Section: A Patient Sample Collectionmentioning

confidence: 99%

“…Along with the current scales such as CDR, MMSE, CASI, MoCA, we used a newly designed Informant-based questionnaire named HAICDDS which is applied in dementia registration in a health system with 9 regional hospitals in Taiwan. Clinical application of the HAICDDS had been published in journals [11]- [13] or conferences [14], [15]. The CDR determined the severity of dementia.…”

Section: A Patient Sample Collectionmentioning

confidence: 99%

“…Neuropsychologists selected 50 items from neuropsychological tests to form an optimal questionnaire for screening patients with varying severities of dementia. While the proposed algorithm still works with the entirety of those features, utilizing feature selection lowers the computational requirements and improves interpretability as we are able to see more clearly which items are directly correlated with a patient's mental condition [11]. In order to retain the features with higher prediction performance, provide faster and more cost-effective predictors, reduce the curse of dimensionality problem and the possibility of overfitting during the training phase, we used information gain feature selection algorithms to rank the importance score of all 50 features and then the low ranking features were filtered out.…”

Section: B Feature Selectionmentioning

confidence: 99%

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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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Section: A Patient Sample Collectionmentioning

confidence: 99%

Section: A Patient Sample Collectionmentioning

confidence: 99%

Section: B Feature Selectionmentioning

confidence: 99%

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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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“…Detection of dementia (including Alzheimer's disease) [20][21][22] Detection of anxiety [23][24][25] Medicalchain-Blockchain and healthcare [26][27][28] Genetics and genomics [29][30][31] Virtual reality in healthcare [32][33][34] Social media in healthcare [35][36][37] Robotic surgery [38][39][40]…”

Section: Emergent Topics Recommended Readingsmentioning

confidence: 99%

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

2019

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The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity. A lot of artificial intelligence techniques have been demonstrated to be effective in smart city applications like energy, transportation, retail and control. In recent decade, retardation of the adoption of data analytics algorithms and systems in healthcare has been decreasing, and there is tremendous growth in data analytics research on healthcare data. The results of analytics aim at improving people's quality of life as well as relieving the issue of medical shortages. In this special issue "Data Analytics in Smart Healthcare", thirteen (13) papers have been published as the representative examples of recent developments. Guest Editors also highlight some emergent topics and opening challenges in healthcare analytics which follow the visions of the movement of healthcare analytics research.

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“…Finally, Section 6 concludes the article highlighting its main contributions and our future direction. [1] MCI refers to the condition where an older adult experiences cognitive impairment especially in tasks related to orientation and judgment [11] 2 Background…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Assess Language Impairments in Older Adults

Parsa¹,

Alam²,

Mihailidis³

2020

Preprint

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Objectives: The main objective of this paper is to propose a methodology based on machine learning classifiers for assessing language impairments associated with dementia in older adults. To do so, we compare the impact of different types of language tasks, features, and recording media on our ML-based methodology’s efficiency. Methodology: The methodology encompasses the following steps: 1) Extracting linguistic and acoustic features from subjects’ speeches which have been collected from subjects with dementia ( N =9) and subjects without dementia ( N =13); 2) Employing feature selection methods to rank informative features; 3) Training ML classifiers using extracted features to recognize subjects with dementia from subjects without dementia; 4) Evaluating the classifiers; 5) Selecting the most accurate classifiers to develop the languages assessment tools. Results: Our results indicate that 1) we can find more predictive linguistic markers to distinguish language impairment associated with dementia from participants’ speech produced during the picture description language task than the story recall task. 2) a phone-based recording interface provides a more high-quality language dataset than the web-based recording systems; 3) classifiers trained with selected features from acoustic features or linguistic features show higher performance than the classifiers trained with pure features. Conclusion: Our results show that the tree-based classifiers that have been trained using the PD dataset can be used to develop an ML-based language assessment tool that can detect language impairment associated with dementia as quickly as possible.

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NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia

Cited by 41 publications

References 31 publications

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

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

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

Machine Learning to Assess Language Impairments in Older Adults

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