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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

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

DOI:
10.3791/59649-v

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12:18 min

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January 11, 2020

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, , , ,
1Embedded Systems and Artificial Intelligence Group,Universidad CEU Cardenal Herrera, 2Community Pharmacist, 3Department of Pharmacy,Universidad CEU Cardenal Herrera

Capítulos

  • 00:00Título
  • 00:50Selection of Factors Associated with MCI
  • 02:18Design of the Questionnaires
  • 02:50Selection of Tests for MCI Screening
  • 03:27Subject’s Recruitment
  • 04:26Pharmacist Researchers Training
  • 04:37Study Design
  • 05:00Interdisciplinary Communication Network, Pharmacist, Primary Care Physician and Specialist
  • 05:18Statistical Analysis and Preprocessing
  • 06:04Algorithms to Create a Decision Tree
  • 07:01Representative Results
  • 10:36Discussion

Summary

Traducción Automática

Traducción Automática

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Tags

Keywords: Cognitive ImpairmentMild Cognitive ImpairmentDementiaScreeningRisk FactorsQuestionnaireMachine LearningCost-effectivePrimary Health CareATC CodeLifestyleChronic PathologiesDepression

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