ArticleJournal of the American Medical Informatics Association : JAMIA2022
Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low-Income Populations Within United States Health Systems: A Scoping Review.Health science reports · 2026Article
- Decision-making for older adults with advanced heart disease: a framework for balancing benefit versus futility.European heart journal · 2026Review
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Information Distortion in Electronic Health Records: A Concept Analysis.Journal of advanced nursing · 2026Review
- Article
- Language Barriers and Other Determinants of Post-Intensive Care Unit Follow-Up: A 5-Year Review.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Proceedings of the second Artificial Intelligence in Primary Immunodeficiency (AIPI) meeting.The Journal of allergy and clinical immunology · 2026Article
- Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.The American journal of bioethics : AJOB · 2026Article
- Evaluating sociodemographic bias in a deployed machine-learned patient deterioration model.JAMIA open · 2025Article
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- Re: "the challenges of measuring socioeconomic inequality in pharmacoepidemiology studies".American journal of epidemiology · 2025Article
- Evaluating algorithmic fairness of machine learning models in predicting underweight, overweight, and adiposity across socioeconomic and caste groups in India: evidence from the longitudinal ageing study in India.PLOS digital health · 2025Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Review
- Transforming Cancer Care: A Narrative Review on Leveraging Artificial Intelligence to Advance Immunotherapy in Underserved Communities.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveArtificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. MATERIALS AND
methodsThis study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES.
resultsAsthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2-Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). DISCUSSION: Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias.
conclusionThe HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.
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