The management of hyperglycemia in hospitalized patients has a significant
impact on both morbidity and mortality. Therefore, it is important to predict
the need for diabetic patients to be hospitalized. However, using standard
machine learning approaches to make these predictions may result in health
disparities caused by biases in the data related to social determinants (such
as race, age, and gender). These biases must be removed early in the data
collection process, before they enter the system and are reinforced by model
predictions, resulting in biases in the model's decisions. In this paper, we
propose a machine learning pipeline capable of making predictions as well as
detecting and mitigating biases in the data and model predictions. This
pipeline analyses the clinical data and determines whether biases exist in the
data, if so, it removes those biases before making predictions. We evaluate the
performance of the proposed method on a clinical dataset using accuracy and
fairness measures. The findings of the results show that when we mitigate
biases early during the data ingestion, we get fairer predictions.

研究探讨了使用机器学习方法提前预测糖尿病患者住院需求的重要性，同时阐述了在数据收集和模型预测中消除社会相关的数据偏差的必要性。该研究提出了一种机器学习流程，能够检测并缓解数据和模型预测中的偏差，从而获得更加公正的预测结果。实验证明，及时消除数据收集过程中的偏差可以得到更加公平的预测结果。