scholarly journals Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies

2020 ◽  
Vol 10 (3) ◽  
pp. 183
Author(s):  
Alexander Pilozzi ◽  
Xudong Huang
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Data Transfer ◽  
Test Results ◽  
The Public ◽  
Medical Institutions ◽  
Prevention Method ◽  
And Storage

Alzheimer’s disease (AD) imposes a considerable burden on those diagnosed. Faced with a neurodegenerative decline for which there is no effective cure or prevention method, sufferers of the disease are subject to judgement, both self-imposed and otherwise, that can have a great deal of effect on their lives. The burden of this stigma is more than just psychological, as reluctance to face an AD diagnosis can lead people to avoid early diagnosis, treatment, and research opportunities that may be beneficial to them, and that may help progress towards fighting AD and its progression. In this review, we discuss how recent advents in information technology may be employed to help fight this stigma. Using artificial intelligence (AI) technologies, specifically natural language processing (NLP), to classify the sentiment and tone of texts, such as those of online posts on various social media sites, has proven to be an effective tool for assessing the opinions of the general public on certain topics. These tools can be used to analyze the public stigma surrounding AD. Additionally, there is much concern among individuals that an AD diagnosis, or evidence of pre-clinical AD such as a biomarker or imaging test results, may wind up unintentionally disclosed to an entity that may discriminate against them. The lackluster security record of many medical institutions justifies this fear to an extent. Adopting more secure and decentralized methods of data transfer and storage, and giving patients enhanced ability to control their own data, such as a blockchain-based method, may help to alleviate some of these fears.

scholarly journals Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

2020 ◽  
Vol 78 (4) ◽  
pp. 1547-1574
Author(s):  
Sofia de la Fuente Garcia ◽  
Craig W. Ritchie ◽  
Saturnino Luz
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Future Research ◽  
Original Research ◽  
Speech And Language ◽  
Eligibility Criteria ◽  
Speech And Language Processing

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

Evaluation of macrolinguistic aspects of the oral discourse in patients with Alzheimer’s disease

2018 ◽  
Vol 31 (9) ◽  
pp. 1343-1353 ◽  
Author(s):  
Juliana Onofre de Lira ◽  
Thaís Soares Cianciarullo Minett ◽  
Paulo Henrique Ferreira Bertolucci ◽  
Karin Zazo Ortiz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Cross Sectional Study ◽  
Oral Discourse ◽  
Cross Sectional ◽  
Cohesive Devices ◽  
Global Coherence ◽  
Comparison Groups ◽  
Study Setting

ABSTRACTIntroduction:Alzheimer’s disease (AD) is a degenerative syndrome that impairs cognitive functioning, including speech and language. Discourse can be used to analyze language processing, which is organized into microlinguistic and macrolinguistic dimensions.Objectives:To identify the occurrence of changes in the macrolinguistic dimension of oral discourse in AD patients. Design: This was developed as a cross-sectional study. Setting: Outpatient clinic of the Behavioural Neurology Division of São Paulo Federal University.Participants:121 elderly patients, with ≥ 4 years of education, divided into AD and comparison groups.Measurements:The subjects were asked to create a narrative based on seven figures that made up a story. The macrolinguistic aspects of the narratives were analyzed.Results:The performance of the AD group was inferior to that of the comparison group on content-related, no-content-related complete and incomplete propositions as well as macropropositions, main information units, appropriated local and global coherence, cohesive devices and all subtypes, cohesive errors and some of their subtypes. Global coherence, macropropositions and ellipsis subtype of cohesive devices were the variables that best differentiated the groups.Conclusions:Changes were observed in most aspects of the macrolinguistic dimension of oral discourse in patients with AD.

Toward a More Nuanced Perception of Alzheimer’s Disease

2012 ◽  
Vol 27 (6) ◽  
pp. 388-396 ◽  
Author(s):  
Baldwin Van Gorp ◽  
Tom Vercruysse ◽  
Jan Van den Bulck
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Experimental Design ◽  
Fear Of Death ◽  
The Public ◽  
Starting Point

Starting point of this study was the assumption that Alzheimer’s disease is made worse for the person who has the disease by the negative regard in which the illness is held by society. The aim was to test by means of a campaign advertisement whether more nuanced counterframes could have an impact while remaining credible and comprehensible to the public. A sample of thousand people living in Belgium evaluated the campaign in an experimental design. This revealed that all the versions tested achieved a high average evaluation. The ad in which the heading referred to the fear of death and degeneration was judged to be most attention-grabbing, easier to understand, and more credible than the alternative heading with the idea that someone with Alzheimer’s could still enjoy playing cards. Together, these findings provided a basis for the use of counterframes to generating a more nuanced image of Alzheimer’s disease.

Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion

2019 ◽  
Vol 33 (05) ◽  
pp. 1957001
Author(s):  
Yin Dai ◽  
Daoyun Qiu ◽  
Yang Wang ◽  
Sizhe Dong ◽  
Hong-Li Wang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Effective Means ◽  
Computer Aided Diagnosis ◽  
Test Results ◽  
Back Propagation Algorithm ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.

scholarly journals The Impact of Experience with a Family Member with Alzheimer’s Disease on Views about the Disease across Five Countries

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Robert J. Blendon ◽  
John M. Benson ◽  
Elizabeth M. Wikler ◽  
Kathleen J. Weldon ◽  
Jean Georges ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Family Member ◽  
Public Spending ◽  
The United States ◽  
Direct Experience ◽  
Fatal Disease ◽  
The Public ◽  
Educational Campaigns ◽  
The Impact

The objective of this paper is to understand how the public’s beliefs in five countries may change as more families have direct experience with Alzheimer’s disease. The data are derived from a questionnaire survey conducted by telephone (landline and cell) with 2678 randomly selected adults in France, Germany, Poland, Spain, and the United States. The paper analyzes the beliefs and anticipated behavior of those in each country who report having had a family member with Alzheimer’s disease versus those who do not. In one or more countries, differences were found between the two groups in their concern about getting Alzheimer’s disease, knowledge that the disease is fatal, awareness of certain symptoms, and support for increased public spending. The results suggest that as more people have experience with a family member who has Alzheimer’s disease, the public will generally become more concerned about Alzheimer’s disease and more likely to recognize that Alzheimer’s disease is a fatal disease. The findings suggest that other beliefs may only be affected if there are future major educational campaigns about the disease. The publics in individual countries, with differing cultures and health systems, are likely to respond in different ways as more families have experience with Alzheimer’s disease.

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