ArticleJMIR public health and surveillance2020
A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study.
Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Evaluating algorithmic fairness in the performance of the AHA PREVENT equations versus pooled cohort equations among single-race and multiracial populations.American journal of preventive cardiology · 2026Article
- Review
- Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.JAMIA open · 2026Review
- Performance of 4 Methods to Assess Health-Related Social Needs.JAMA network open · 2025Article
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- Using Under-Represented Subgroup Fine Tuning to Improve Fairness for Disease Prediction.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2025Article
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.Journal of medical Internet research · 2024Article
- Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.Journal of biomedical informatics · 2024Article
- Leveraging graph neural networks for supporting automatic triage of patients.Scientific reports · 2024Article
- Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool.Applied clinical informatics · 2024Article
- Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis.Journal of medical Internet research · 2024Article
- Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.Critical care explorations · 2024Article
- Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.Circulation. Heart failure · 2024Article
- Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.Health affairs (Project Hope) · 2023Article
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).medRxiv : the preprint server for health sciences · 2023Article
- Improving child health through Big Data and data science.Pediatric research · 2023Review
- Fairness in Mobile Phone-Based Mental Health Assessment Algorithms: Exploratory Study.JMIR formative research · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRacial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups.
objectiveThe goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups.
methodsBias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care-III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE).
resultsThe machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively).
conclusionsThis study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.
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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.