ArticleJournal of biomedical informatics2023
Fair patient model: Mitigating bias in the patient representation learned from the electronic health records.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Challenges in medical algorithmic fairness.JNCI cancer spectrum · 2026Review
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Fair and robust early readmission risk prediction from electronic health records via diffusion-based data augmentation and causal-invariant representation learning.Scientific reports · 2026Article
- Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques.Scientific reports · 2026Article
- Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.Neural computing & applications · 2026Article
- A multimodal generative model for structured and unstructured electronic health records.npj health systems · 2026Article
- FairICP: identifying biases and increasing transparency at the point of care in post-implementation clinical decision support using inductive conformal prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework.JMIR AI · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo 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.
methodsWe 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.
resultsFPM 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.
conclusionFPM is a novel method to pre-train fair and unbiased patient representations from the 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 fairness is important.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.