scholarly journals Alzheimer’s Disease: The Relative Importance Diagnostic

2020 ◽  
Vol 09 (04) ◽  
pp. 77-86
Author(s):  
Maryam Habadi ◽  
Christos P. Tsokos
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Relative Importance

Relative importance of patient disease indicators on informal care and caregiver burden in Alzheimer's disease

2010 ◽  
Vol 23 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Niklas Bergvall ◽  
Per Brinck ◽  
Daniel Eek ◽  
Anders Gustavsson ◽  
Anders Wimo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Informal Care ◽  
Caregiver Burden ◽  
Community Dwelling ◽  
Relative Importance ◽  
Behavioral Disturbances ◽  
Cross Sectional ◽  
Disease Indicators ◽  
Zarit Burden Inventory

ABSTRACTBackground: Cognition, abilities in activities of daily living (ADL), and behavioral disturbances in patients with Alzheimer's disease (AD) all influence the number of hours informal caregivers spend caring for their patients, and the burden caregivers experience. However, the direct effect and relative importance of each disease severity measure remains unclear.Methods: Cross-sectional interviews were conducted with 1,222 AD patients and primary caregivers in Spain, Sweden, the U.K. and the U.S.A. Assessments included informal care hours, caregiver burden (Zarit Burden Inventory; ZBI), cognition (Mini-mental State Examination; MMSE), ADL-abilities (Disability Assessment for Dementia scale; DAD), and behavioral symptoms (Neuropsychiatric Inventory Questionnaire; NPI-severity).Results: Multivariate analyses of 866 community-dwelling patients revealed that ADL-ability was the strongest predictor of informal care hours (36% decrease in informal care hours per standard deviation (SD) increase in DAD scores). Severity of behavioral disturbances was the strongest predictor of caregiver burden (0.35 SD increase in ZBI score per SD increase in NPI-Q severity score). In addition, the effect of ADL-abilities was, although attenuated, not negligible (0.28 SD increase in ZBI score per SD increase in DAD score). Decreasing cognition (MMSE) was associated with more informal care hours and increased caregiver burden in univariate, but not in adjusted analyses.Conclusions: For patients residing in community dwellings, the direct influence of patients’ cognition on caregiver burden is limited and rather mediated by other disease indicators. Instead, the patients’ ADL-abilities are the main predictor of informal care hours, and both ADL-abilities and behavioral disturbances are important predictors of perceived caregiver burden, where the latter has the strongest effect. These results were consistent across Sweden, U.K. and the U.S.A.

scholarly journals Explainable and Argumentation-based Decision Making with Qualitative Preferences for Diagnostics and Prognostics of Alzheimer's Disease

2020 ◽  
Author(s):  
Zhiwei Zeng ◽  
Zhiqi Shen ◽  
Benny Toh Hsiang Tan ◽  
Jing Jih Chin ◽  
Cyril Leung ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Decision Making ◽  
Automated Reasoning ◽  
Good Precision ◽  
Relative Importance ◽  
Learning Models ◽  
Real World Datasets ◽  
Machine Learning Models ◽  
Qualitative Preferences

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.

scholarly journals Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

2020 ◽  
Author(s):  
Ning Wang ◽  
Mingxuan Chen ◽  
K.P. Subbalakshmi
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Automated Detection ◽  
Relative Importance ◽  
Language Abilities ◽  
Attention Networks ◽  
Part Of Speech ◽  
Speech Features ◽  
Learning Architectures

AbstractIn this work we propose three explainable deep learning architectures to automatically detect patients with Alzheimer’s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional Convolutional Neural Network (CNN) to generate two types of explanations of the model’s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentations and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952 on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.

Neurochemical Correlates of Dementia Severity in Alzheimer's Disease: Relative Importance of the Cholinergic Deficits

2002 ◽  
Vol 64 (2) ◽  
pp. 749-760 ◽  
Author(s):  
Linda M. Bierer ◽  
Vahram Haroutunian ◽  
Steve Gabriel ◽  
Peter J. Knott ◽  
Lorna S. Carlin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Relative Importance ◽  
Dementia Severity ◽  
Neurochemical Correlates

Understanding the relative importance of preserving functional abilities in Alzheimer’s disease in the United States and Germany

2014 ◽  
Vol 23 (6) ◽  
pp. 1813-1821 ◽  
Author(s):  
A. Brett Hauber ◽  
Ateesha F. Mohamed ◽  
F. Reed Johnson ◽  
Michael Cook ◽  
H. Michael Arrighi ◽  
...  
Keyword(s):  
United States ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The United States ◽  
Relative Importance ◽  
Functional Abilities
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