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Related Concept Videos

Dementia01:30

Dementia

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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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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Quad-phased data mining modeling for dementia diagnosis.

Sunjoo Bang1, Sangjoon Son2, Hyunwoong Roh2

  • 1Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea.

BMC Medical Informatics and Decision Making
|May 26, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data mining model to improve dementia diagnosis. The system assists clinicians by providing intuitive insights, reducing diagnosis time and costs.

Keywords:
Artificial neural networkComputer-aided diagnosis (CAD)Data mining modelingDecision treeDementia diagnosisSupport vector machineTree visualizationVariable selection

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Area of Science:

  • Gerontology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Global population aging is increasing dementia prevalence.
  • Current dementia diagnosis relies on time-consuming, subjective physician evaluations.
  • Computer-aided diagnosis (CAD) offers potential solutions for objective dementia assessment.

Purpose of the Study:

  • To develop and evaluate a data mining model for enhanced dementia diagnosis.
  • To address the limitations of subjective and time-intensive traditional diagnostic methods.
  • To support clinical decision-making through an intuitive CAD system.

Main Methods:

  • A quad-phased data mining model comprising Proposer, Predictor, Descriptor, and Visualization modules.
  • Utilizing machine learning algorithms for dementia prediction and diagnosis.
  • Profiling patient groups to interpret diagnostic causes and enhance understanding.

Main Results:

  • The proposed model was applied to the CREDOS study dataset.
  • The study utilized clinical data from 37 Korean university-affiliated hospitals (2005-2013).

Conclusions:

  • The developed system facilitates intelligent collaboration between CAD and physicians.
  • The enhanced evaluation process significantly reduces diagnostic time and costs for clinicians and patients.