Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important.

本文提出了一种名为公平患者模型（FPM）的新方法，在深度表示学习模型中使用一种称为加权损失函数的方法，降低偏见并提高公平性，从电子健康记录（EHRs）中预先训练公平和无偏的患者表示，在各种医疗保健下游任务中有广泛应用，同时还可扩展到其他因偏见和公平性重要性高的领域。

公平患者模型：减轻从电子健康记录中学习到的患者代表性偏见