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Related Concept Videos

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Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Updated: Sep 8, 2025

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An artificial intelligence-based platform for personalized predictions of Metacognitive Training effectiveness.

Caroline König1, Pedro Copado1, Alfredo Vellido1

  • 1Soft Computing Research Group (SOCO), Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Centre, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Jordi Girona 1-3, Barcelona, 08034, Spain.

Computational and Structural Biotechnology Journal
|August 20, 2025
PubMed
Summary

This study presents a machine learning platform to predict Metacognitive Training (MCT) effectiveness for psychosis patients. It uses explainable AI and bias analysis to support personalized treatment decisions.

Keywords:
Explainable artificial intelligenceFairnessFeature selectionMental healthMetacognitive TrainingPersonalized medicine

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Area of Science:

  • Psychiatry
  • Computer Science
  • Artificial Intelligence

Background:

  • Metacognitive Training (MCT) is a therapeutic approach for psychosis.
  • Personalizing treatment plans is crucial for improving patient outcomes.
  • Existing decision support systems may lack comprehensive predictive capabilities.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML)-based platform for predicting MCT effectiveness.
  • To create a prototype decision support system for clinicians treating psychosis patients.
  • To enhance treatment personalization through data-driven insights.

Main Methods:

  • Integration of eight ML models to predict MCT effectiveness.
  • Utilizing a wide range of mental health questionnaires to assess diverse psychological symptoms.
  • Implementation of explainable AI (XAI) using SHAP analysis for model transparency.
  • Conducting disparate impact analysis to ensure gender-neutral model behavior.

Main Results:

  • The platform integrates multiple ML models for comprehensive patient profiling.
  • Explainable AI methods provide transparency into predictive model reasoning.
  • Disparate impact analysis addresses ethical considerations and potential biases.
  • The system aims to support tailored treatment planning for psychosis.

Conclusions:

  • The developed ML platform shows promise as an experimental prototype for predicting MCT effectiveness.
  • The integration of XAI and ethical bias analysis aligns with regulatory requirements (e.g., EU AI Act).
  • This approach can advance personalized medicine in mental healthcare by supporting clinical decision-making.