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Related Concept Videos

Dementia01:30

Dementia

148
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
549

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Related Experiment Video

Updated: Aug 1, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study.

Sinan Erturk1,2, Georgie Hudson1,2, Sonja M Jansli1,2

  • 1Institute of Psychiatry, Psychology & Neuroscience King's College London London United Kingdom.

JMIR Infodemiology
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models can identify dementia misconceptions on Twitter. While an awareness campaign was ineffective, these models can help future campaigns adapt to real-time events influencing public understanding.

Keywords:
Twittercodevelopmentmachine learningmisconceptionspatient and public involvementsocial mediastigma

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Area of Science:

  • Computational linguistics
  • Public health communication
  • Artificial intelligence in social science

Background:

  • Dementia misinformation on social media platforms like Twitter can negatively impact public perception and awareness.
  • Machine learning (ML) models, co-developed with individuals with lived experience (carers), offer a viable method for identifying and analyzing such misinformation.
  • Accurate identification of misconceptions is crucial for evaluating the effectiveness of public health campaigns.

Purpose of the Study:

  • To develop and validate a machine learning model capable of distinguishing between dementia-related misconceptions and neutral content in tweets.
  • To design, implement, and assess the impact of a public awareness campaign aimed at combating dementia misconceptions.
  • To analyze the influence of real-world events on the prevalence of dementia misconceptions on Twitter.

Main Methods:

  • Four machine learning models were constructed using 1414 tweets previously rated by carers.
  • Model performance was evaluated using 5-fold cross-validation and a subsequent blind validation with carers.
  • An awareness campaign was co-developed and deployed, with pre- and post-campaign tweets (N=4880) classified by the best-performing ML model. A total of 7124 UK dementia tweets were analyzed.

Main Results:

  • A random forest model achieved 82% accuracy in identifying misconceptions during blind validation.
  • Across the campaign period, 37% of UK dementia tweets (N=7124) were classified as misconceptions.
  • Misconception prevalence increased around political topics, peaking during government-related controversies, and the awareness campaign did not significantly alter misconception rates.

Conclusions:

  • An accurate machine learning model for predicting dementia tweet misconceptions was successfully developed through co-development with carers.
  • The implemented awareness campaign did not significantly reduce the prevalence of misconceptions.
  • Future awareness campaigns could be enhanced by leveraging ML models to dynamically respond to current events that influence public understanding of dementia in real time.