Janson Karina, Gottfried Karl, Reis Olaf, Kornhuber Johannes, Eichler Anna, Deuschle Michael, Banaschewski Tobias, Nees Frauke
Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Institute of Medical Psychology and Medical Sociology, https://ror.org/01tvm6f46University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany.
Eur Psychiatry. 2025 May 26;68(1):e75. doi: 10.1192/j.eurpsy.2025.2457.
Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses.
Here, we introduce , a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies.
The framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data.
The app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field.
如今，研究人员和临床医生都不得不处理越来越大的数据集，尤其是在心理健康数据方面。目前仍缺乏用于可视化来自各种基于项目的工具（如问卷数据、数字应用程序数据或临床文档数据）的复杂工具，特别是在多层次整合以及用于数据组织和为后续分析的有效使用进行适当构建方面。
在此，我们介绍一种基于Python的大型数据集事后可视化框架。该方法利用对工具对齐的全面识别以及基于数据和研究内部及之间的项目相似性以及共享与差异概念基础来识别新的内容网络和图表。
使用来自四项不同队列研究的四个现有大型数据集对该框架进行了评估，结果表明在研究内部和研究之间跨多项目工具成功实现了数据可视化。该框架使研究人员和临床医生能够可靠、信息丰富且快速地浏览大型数据集。此外，它有助于提取有关数据中结构表示和概念识别的新见解。
该应用程序是大数据管理和分析领域的一个有效工具，可应对现代数据集日益增长的复杂性，以挖掘这些大量信息集合中隐藏的潜力。它在研究和临床领域也很容易根据单个数据集和用户偏好进行调整。
