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Updated: Jul 13, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Case studies of clinical decision-making through prescriptive models based on machine learning.

William Hoyos1, Jose Aguilar2, Mayra Raciny3

  • 1Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.

Computer Methods and Programs in Biomedicine
|October 14, 2023
PubMed
Summary

This study introduces a novel computational method using fuzzy cognitive maps and optimization algorithms to create prescriptive models for disease monitoring, treatment, and prevention, aiding clinical decision-making.

Keywords:
Artificial intelligenceClinical decision-makingPredictive modelPrescriptive model

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Area of Science:

  • Computational intelligence in healthcare
  • Artificial intelligence for clinical decision support

Background:

  • Clinical decision-making is crucial for reducing patient morbidity and mortality.
  • Prescriptive analytics offers a promising approach for disease monitoring, treatment, and prevention.
  • Challenges persist for medical professionals in effectively managing these aspects.

Purpose of the Study:

  • To propose a methodology for developing prescriptive models to aid clinical decision-making.
  • To integrate predictive and prescriptive modeling for comprehensive health support.
  • To address challenges in disease management through computational approaches.

Main Methods:

  • Developed a predictive model using fuzzy cognitive maps and particle swarm optimization.
  • Created a prescriptive model by extending fuzzy cognitive maps with genetic algorithms.
  • Evaluated the methodology through three distinct case studies: warfarin dose estimation, severe dengue treatment, and geohelminthiasis prevention.

Main Results:

  • Prescriptive models successfully estimated warfarin doses, prescribed severe dengue treatment, and suggested geohelminthiasis prevention strategies.
  • Predictive models accurately forecast coagulation indices, severe dengue mortality risk, and soil-transmitted helminth infection prevalence.
  • The integrated approach demonstrated efficacy across diverse clinical scenarios.

Conclusions:

  • The developed models effectively support decision-making for disease monitoring, treatment, and prevention.
  • This computational strategy enhances clinical decision support systems.
  • Further validation in real-world healthcare settings is necessary for implementation.