Introduction:

Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance.

Method:

To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints.

Results:

We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction.

Conclusion:

Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.

中文翻译：

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Trans-Balance：在存在阶级不平衡的情况下减少预测模型的人口差异

介绍：

风险预测，包括疾病的早期发现、预防和干预，对于精准医疗至关重要。然而，不同人口群体的异质性导致的风险评估系统性偏差可能会导致不适当或误导性的治疗决策。此外，低发生率（类别不平衡）结果会对许多标准学习算法的分类性能产生负面影响，从而进一步加剧种族差异问题。因此，在存在严重阶级不平衡的情况下，提高代表性不足人群的统计和机器学习模型的性能至关重要。

方法：

为了解决阶级不平衡导致的人口不平等问题，我们利用近期在不平衡方面取得的进展，开发了一个新颖的框架：Trans-Balance学习、迁移学习和联邦学习。我们考虑了一种实用的设置，其中来自多个站点的数据在隐私限制下存储在本地。

结果：

我们表明，所提出的Trans-Balance框架通过明确考虑人口亚组和群体之间的异质性，改进了现有方法。我们通过数值实验和对多队列研究的实际应用来证明我们的方法的可行性和有效性，该研究的数据来自 NIH 资助的四个大型队列的参与者，用于中风风险预测。

结论：

我们的研究结果表明，Trans-Balance 方法可显着提高预测性能，特别是在阶级不平衡和人口结构严重差异的情况下。鉴于其多功能性和有效性，Trans-Balance 为增强生物医学研究及相关领域的风险预测做出了宝贵贡献。