Araújo Rúben, Ramalhete Luís, Viegas Ana, Von Rekowski Cristiana P, Fonseca Tiago A H, Calado Cecília R C, Bento Luís
NMS-NOVA Medical School, FCM-Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal.
CHRC-Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal.
Methods Protoc. 2024 Apr 24;7(3):36. doi: 10.3390/mps7030036.
Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.
强大的数据归一化和分析在生物医学研究中至关重要，以确保观察到的人群差异直接归因于目标变量，而不是对照组和研究组之间的差异。ArsHive使用先进算法来解决这一挑战，对人群（如对照组和研究组）进行归一化，并对生物医学数据集中的人口统计学、临床和其他变量进行统计评估，从而实现更平衡、无偏差的分析。该工具的功能扩展到全面的数据报告，既能阐明数据处理的效果，又能保持数据集的完整性。此外，ArsHive还辅以A.D.A.（自主数字助理），它采用OpenAI的GPT-4模型协助研究人员进行查询，增强决策过程。在这项概念验证研究中，我们在来自专有数据的三个不同数据集上测试了ArsHive，证明了它在管理复杂临床和治疗信息方面的有效性，并突出了其在不同研究领域的通用性。
