数据协调用于分析个性化治疗精神病与元认知训练的分析
Caroline König1, Pedro Copado2, Maria Lamarca3,4,5
1Soft 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. caroline.leonore.konig@upc.edu.
Scientific reports
|March 25, 2025
概括
这项研究协调了22项研究的数据,创建了一个统一的患者数据库,用于精神病治疗中的个性化医学. 这使得针对量身定制的超认知训练 (MCT) 疗法的高级数据分析成为可能.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 个性化医疗需要全面的患者数据,以便量身定制的诊断和治疗.
- 机器学习和预测模型对于推进个性化治疗方法至关重要.
- 数据协调是数据处理中一个关键步骤,用于可靠的分析.
研究的目的:
- 为精神病患者进行超认知训练 (MCT) 的患者开发一个统一的回顾数据库.
- 将多项研究的数据整合到一个数据驱动的个性化医疗平台的共同格式中.
- 支持PERMEPSY项目的目标,即为精神病治疗创造个性化医学方法.
主要方法:
- 应用了数据协调技术来整合来自22个不同的研究的信息.
- 建立了一个回顾性数据库,包括698名接受MCT的患者的数据.
- 用描述性统计和探索性数据分析来检查患者的特征.
主要成果:
- 成功创建了一个统一,协调的数据库,整合了22项研究的数据.
- 该数据库包括698名接受MCT治疗的患者的社会人口统计学变量和心理指标.
- 初步分析提供了对协调数据集中的患者特征的见解.
结论:
- 数据协调是可行的,对于建立强大的心理病个性化医学数据库至关重要.
- 开发的协调数据库是个性化心理治疗中数据分析方法的基础.
- 这项工作推动了针对精神病的个性化医疗平台的开发,与PERMEPSY项目目标保持一致.
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