State Dementia Plans and the Alzheimer’s Disease Movement: Framing Diagnosis, Prognosis, and Motivation

2015 ◽  
Vol 36 (7) ◽  
pp. 840-863 ◽  
Author(s):  
Charlotte E. Arbogast ◽  
E. Ayn Welleford ◽  
F. Ellen Netting
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Call To Action ◽  
Interpretive Analysis ◽  
Raising Awareness ◽  
Centered Care ◽  
Similarities And Differences ◽  
Primary Focus ◽  
Set Up ◽  
The Cost

An interpretive analysis of 38 state dementia plans compares similarities and differences in diagnostic framing (problem identification/trends/issues), prognosis framing (addressing the problem), and motivational framing (calls for action) across plans. In framing diagnosis, only 6 plans used dementia alone in their titles. In framing prognosis and the subsequent call to action, state plans were consistent in their dire prognostications about the progressive and fatal consequences of the disease with a primary focus on the cost. Motivational language mirrored that of the Alzheimer’s Disease (AD) Movement, from raising awareness to using inflammatory words to incite action. The language used set up the frame for clinical interventions that may not distinguish between types of dementia and could undercut the provision of person-centered care, shifts the victimization focus from persons with AD to caregivers and ultimately the state, and may subintentionally reflect cultural biases.

The cost-utility of the rivastigmine transdermal patch in the management of patients with moderate Alzheimer's disease in the US

2009 ◽  
Vol 36 (S 02) ◽  
Author(s):  
A Brennan ◽  
B Nagy ◽  
A Brandtmüller ◽  
SK Thomas ◽  
M Gallagher ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cost Utility ◽  
Transdermal Patch ◽  
The Us ◽  
The Cost

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

The Cost of Recruiting Alzheimer’s Disease Caregivers for Research

2000 ◽  
Vol 12 (4) ◽  
pp. 490-510 ◽  
Author(s):  
Barbara Amelotte Tarlow ◽  
Diane Feeney Mahoney
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The Cost

Comprehensive review on Alzheimer's Disease: Pathophysiology and its Treatment

2021 ◽  
Vol 4 (3) ◽  
pp. 3-12
Author(s):  
Prativa Sadhu ◽  
◽  
Srijani Sen ◽  
Catherine Vanlalhriatpuii ◽  
◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Signs ◽  
Acetylcholinesterase Inhibitors ◽  
Neuron Activity ◽  
Aβ Peptides ◽  
Comprehensive Review ◽  
Thinking Ability ◽  
The Cost ◽  
Amyloid Cascade

Neurodegenerative disorders are marked by the loss of brain neuron activity, resulting in gradual cognitive impairment. The effects of neurodegenerative diseases are severe in terms of pathology and the cost of patient care. The aged, in general, are the most vulnerable. Alzheimer's disease (AD) is a brain ailment that causes cell degradation and is the leading cause of dementia, identified by a loss of thinking ability and independence in daily tasks. The amyloid cascade hypothesis, which attributes clinical signs/symptoms to an abundance of amyloid-beta (Aβ) peptides, enhanced deposition into amyloid plaques, and eventually neuronal destruction, is one theory for pathogenesis AD. The use of acetylcholinesterase inhibitors in AD treatment is based on their favorable effects on the disease's functional, cognitive and behavioral symptoms. However, their involvement in AD pathogenesis is uncertain. This comprehensive review will provide an overview of AD, including the pathophysiology, causes, treatments, and future treatment.

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