Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction.

中文翻译：

---

通过协调和增量学习克服阻碍临床数据互连和分析的障碍

目标：提出一个数据共享、管理、协调和联合数据分析框架，以解决医疗保健领域的开放性问题，例如开发强大的疾病预测模型。方法：应用数据管理来消除数据不一致。词汇和语义匹配方法用于对齐异构、精选队列数据的结构以及增量学习算法，包括类不平衡处理和超参数优化，以支持疾病预测模型的开发。结果：该框架的适用性在原发性干燥综合征的案例研究中得到证明，产生了质量更高、一致性超过 85% 的协调数据，以及灵敏度和特异性超过 80% 的淋巴瘤预测模型。结论：该框架提供了数据质量、协调和分析工作流程，可以增强异构临床数据的统计能力，并能够开发用于疾病预测的稳健模型。