König Caroline, Copado Pedro, Lamarca Maria, Guendouz Wafaa, Fischer Rabea, Schlechte Merle, Acuña Vanessa, Berna Fabrice, Gawęda Łucasz, Vellido Alfredo, Nebot Àngela, Angulo Cecilio, Ochoa Susana
Soft Computing Research Group (SOCO) at Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Centre, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Jordi Girona 1-3, 08034, Barcelona, Spain.
MERITT Group, Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, 08830, Sant Boi de Llobregat, Barcelona, Spain.
Sci Rep. 2025 Mar 24;15(1):10159. doi: 10.1038/s41598-025-94815-3.
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.
个性化医疗是一种数据驱动的方法，旨在使患者的诊断和治疗适应其特征和需求。因此，患者数据的可用性对于基于预测模型的治疗个性化至关重要，在基于机器学习的分析中更是如此。数据协调是数据管理过程的重要组成部分。本研究介绍了为开发一个用于精神障碍元认知训练（MCT）治疗患者的统一回顾性数据库而进行的数据协调研究。这项工作是欧洲ERAPERMED 2022 - 292研究项目“迈向精神分裂症心理治疗的个性化医疗方法”（PERMEPSY）的一部分，该项目专注于开发一个用于精神分裂症治疗的个性化医疗平台。该研究将来自22项研究的信息整合为通用格式，以实现个性化治疗的数据分析方法。统一数据库包含698名接受MCT治疗患者的信息，包括用于评估患者心理健康状态的广泛社会人口统计学变量和心理指标。使用描述性统计和探索性数据分析对参与研究的患者特征进行分析。
