Data harmonization for the analysis of personalized treatment of psychosis with metacognitive training

Personalized medicine is a data-driven approach that aims to adapt patients' diagnostics and therapies to their characteristics and needs. The availability of patients' data is therefore paramount for the personalization of treatments on the basis of predictive models, and even more so in machine learning-based analyses. Data harmonization is an essential part of the process of data curation. This study presents research on data harmonization for the development of a harmonized retrospective database of patients in Metacognitive Training (MCT) treatment for psychotic disorders. This work is part of the European ERAPERMED 2022-292 research project entitled 'Towards a Personalized Medicine Approach to Psychological Treatment of Psychosis' (PERMEPSY), which focuses on the development of a personalized medicine platform for the treatment of psychosis. The study integrates information from 22 studies into a common format to enable a data analytical approach for personalized treatment. The harmonized database comprises information about 698 patients who underwent MCT and includes a wide range of sociodemographic variables and psychological indicators used to assess a patient's mental health state. The characteristics of patients participating in the study are analyzed using descriptive statistics and exploratory data analysis.