Fair Patient Model: Mitigating Bias in the Patient Representation
  Learned from the Electronic Health Records
Sonish Sivarajkumar Yufei Huang Yanshan Wang
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.