Personality-Driven Adaptive Psychosomatic Treatment Planning via Artificial Intelligence
Keywords:
psychosomatic medicine, personality traits, artificial intelligence, adaptive treatment planning, personalized healthcareAbstract
The objective of this study was to develop and empirically evaluate an artificial intelligence–based framework that integrates personality traits with psychosomatic symptom profiles to generate adaptive, personalized treatment plans in psychosomatic medicine. A mixed-methods, model-development study was conducted using clinical data from adult patients with psychosomatic complaints recruited from outpatient psychosomatic and psychological services in Taiwan. Standardized personality assessments, psychosomatic symptom measures, clinician-rated evaluations, and behavioral indicators were collected and integrated into a secure digital dataset. Machine learning techniques, including unsupervised clustering and supervised predictive modeling, were applied to identify latent personality–symptom patterns and to generate individualized treatment recommendations across multiple psychosomatic intervention modalities. Model performance was evaluated using cross-validation procedures, and explainable AI methods were employed to enhance interpretability and clinical transparency. Unsupervised learning identified four distinct personality–psychosomatic clusters characterized by differential trait configurations and symptom profiles. Predictive modeling demonstrated high classification accuracy and strong discriminative capacity in matching patients to optimal treatment modalities. Inferential analyses indicated that personality traits significantly contributed to treatment recommendation variance beyond symptom severity alone, and adaptive recommendations differed systematically across clusters, supporting the model’s capacity for clinically meaningful personalization. The findings suggest that integrating personality traits into AI-driven psychosomatic treatment planning enables robust patient stratification, improves personalization of intervention strategies, and offers a scalable decision-support approach aligned with contemporary precision medicine principles. This framework represents a promising step toward adaptive, person-centered psychosomatic care that complements clinical expertise and supports iterative treatment optimization.
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