Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records

Objectives: Artificial intelligence (AI) applications utilizing electronic health records (EHRs) have gained popularity, but they also introduce various types of bias. This study aims to systematically review the literature that address bias in AI research utilizing EHR data. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline. We retrieved articles published between January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the Institute of Electrical and Electronics Engineers. We defined six major types of bias and summarized the existing approaches in bias handling. Results: Out of the 252 retrieved articles, 20 met the inclusion criteria for the final review. Five out of six bias were covered in this review: eight studies analyzed selection bias; six on implicit bias; five on confounding bias; four on measurement bias; two on algorithmic bias. For bias handling approaches, ten studies identified bias during model development, while seventeen presented methods to mitigate the bias. Discussion: Bias may infiltrate the AI application development process at various stages. Although this review discusses methods for addressing bias at different development stages, there is room for implementing additional effective approaches. Conclusion: Despite growing attention to bias in healthcare AI, research using EHR data on this topic is still limited. Detecting and mitigating AI bias with EHR data continues to pose challenges. Further research is needed to raise a standardized method that is generalizable and interpretable to detect, mitigate and evaluate bias in medical AI.Comment: 29 pages, 2 figures, 2 tables, 2 supplementary files, 66 reference

Localized sampling for hospital re-admission prediction with imbalanced sample distributions

© 2017 IEEE. Hospital re-admission refers to special medical events that a patient previously discharged from the hospital is readmitted within a short period of time (say 30 days). A re-admission not only downgrades the quality of living of the patient, it also adds significant financial burdens to the health care systems. To date, many systems exist to use computational approaches to predict the likelihood of a patient being readmitted in the future for medical decision assistance. When building predictive models for hospital re-admission prediction, one essential challenge is that sample distributions in the data are severely imbalanced where, typically, less than 10% of patients are likely going to be readmitted in a near future. A predictive model, without considering sample imbalance, will unlikely generate accurate results for prediction. To date, no existing re-admission model has explicitly addressed such data imbalance issues in their systems. In this paper, we consider hospital re-admission prediction with imbalanced sample distributions, and propose to use localized sampling approach to help build accurate predictive models. For localized sampling, we emphasize on samples which are difficult to classify, and allow the sampling process to bias to such instances. Because finding instances difficult to classify requires calculation of distance between instances, and the high dimensionality of Electronic Health Records (EHR) data makes the distance calculation highly ineffective, we propose to use latent topic embedding to reduce the sample from high dimensionality to a handful of low dimensional topic space for effective and accurate calculation of the distance between instances. By using localized sampling to build multiple versions of balanced datasets, we are able to train multiple predictive models and combine their results for prediction. Experiments and comparisons on data collected from several South Florida regional hospitals demonstrate the performance of our method