MentalSquares: a generic bipolar Support Vector Machine for psychiatric disorder classification, diagnostic analysis...

Area of Science:

• Psychiatry and Computational Neuroscience
• Machine Learning in Healthcare

Background:

• Traditional classification of Bipolar Disorders (BPDs) relies on categorical models.
• Existing diagnostic approaches may not fully capture the dynamic equilibrium of mental states.

Purpose of the Study:

• To introduce MentalSquares (MSQs), an equilibrium-based dimensional approach for psychological condition classification.
• To present MSQs as a generic Support Vector Machine (SVM) for enhanced diagnostic analysis, using BPDs as a case study.

Main Methods:

• Developed an equilibrium-based dimensional approach (MSQs) analogous to Support Vector Machines (SVMs).
• Focused on the balance of mental equilibrium poles, diverging from traditional categorical models.
• Utilized public domain clinical examples and empirical knowledge for analytical illustration.

Main Results:

• The MSQ approach demonstrated advantages over existing models for BPD classification.
• Analytical illustrations confirmed the model's theoretical underpinnings.
• Clinical and computerized operability of the MSQ method was successfully demonstrated.

Conclusions:

• MentalSquares (MSQs) presents a novel, advantageous approach to classifying psychological conditions like BPDs.
• The generic SVM-like model offers improved classification by focusing on mental equilibrium.
• MSQs show potential for analyzing neurobiological patterns and drug effects in clinical practice.