Exploratory data analysis for medical treatment of psychosis

This thesis presents an exploratory data analysis (EDA) of a medical treatment for psychosis, specifically focusing on patients who underwent Metacognitive Training (MCT), as part of the PERMEPSY European research project. Psychosis, characterized by a detachment from reality manifesting in delusions and hallucinations, significantly impairs the quality of life, necessitating effective treatment modalities. Despite the benefits of psychological interventions, access to specialized mental health services and the personalization of treatments are still remaining challenges. The primary objective of the research presented in this thesis is to leverage unsupervised machine learning techniques to analyze clinical data from MCT-treated patients, aiming to identify distinct patient profiles that could improve treatment personalization. The study utilizes the PERMEPSY dataset, comprising sociodemographic information and Positive and Negative Syndrome Scale (PANSS) scores of 698 patients before and after MCT intervention. The methodology involves statistical analysis for data understanding, univariate and multivariate outlier detection for data refinement, and clustering algorithms for patient cohort identification. Techniques such as K-Means, agglomerative hierarchical Clustering, and Gaussian Mixture Models (GMM) are employed to discover meaningful patient groupings. The analyses reveal significant insights into the sociodemographic and clinical characteristics of patients, the impact of treatment on symptomatology, and the efficacy of clustering methods in identifying patient subgroups. These findings should contribute to the broader goal of personalized medicine by providing actionable insights for tailored psychosis treatment strategies.