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Related Concept Videos

Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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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Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
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Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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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.
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Dementia01:30

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The progression of dementia is generally gradual.

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Updated: Jul 4, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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Published on: December 15, 2023

Leveraging the J48 Algorithm to Inform Community-Based AI Solutions for African American Dementia Caregiving.

Sunmoo Yoon1, Melissa Patterson2, Frederick Sun3

  • 1General Medicine, Columbia University, New York, NY.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

African Americans prefer AI for hospital diagnostics and faith-based apps, but caution is needed for community-based AI, especially in nutrition. Cultural context, not just caregiving, shapes AI preferences.

Keywords:
AIcommunity-baseddementia caregivinghealth disparity

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Last Updated: Jul 4, 2026

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09:47

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Published on: December 15, 2023

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning Applications
  • Gerontology and Aging Research

Background:

  • Family caregiving for individuals with Alzheimer's disease and related dementias (ADRD) presents unique challenges.
  • Community-based Artificial Intelligence (AI) solutions offer potential benefits but require understanding of user perceptions.
  • Demographic and cultural factors significantly influence the acceptance and perceived risks of AI technologies in caregiving.

Purpose of the Study:

  • To identify demographic and caregiving factors influencing perceptions of community-based AI solutions among family caregivers.
  • To analyze the preferences and risk perceptions of African American family caregivers regarding AI applications.
  • To compare AI preferences across different cultural contexts and specific use cases.

Main Methods:

  • Application of the J48 machine learning algorithm (C4.5) to model AI preferences.
  • Online survey of 572 diverse family members of individuals with ADRD in the U.S.
  • Analysis of race, education, age, and caregiving factors as predictors of AI acceptance and risk perception.

Main Results:

  • Race was the primary predictor of AI support; African Americans favored AI in hospital-based diagnostics and faith-based apps.
  • Highly educated White caregivers (25-34) perceived higher risks with clinical AI; younger caregivers (18-34) showed heightened risk perception for meal-related apps.
  • Model performance (F-measure 0.83, PRC area 0.74) indicated cultural context and use-case specificity drive AI preference over general caregiving factors.

Conclusions:

  • AI preference among caregivers is more influenced by cultural context and specific applications (e.g., clinical, faith-based) than general caregiving circumstances.
  • While African Americans are open to AI development, enthusiasm varies by community setting, necessitating tailored approaches.
  • Prioritize AI in clinical and faith-based settings; exercise caution with nutritional AI due to potential bias perpetuation.