Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression

Park, Yoonyoung; Hu, Jianying; Singh, Moninder; Sylla, Issa; Dankwa‐Mullan, Irene; Koski, Eileen; Das, Amar K.

doi:10.1001/jamanetworkopen.2021.3909

Cited by 69 publications

(72 citation statements)

References 31 publications

(55 reference statements)

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“…In addition, 1 (8%) study used publicly available code for analysis [ 31 ], and code was specified as available upon request in 1 (8%) study [ 35 ]. Bias of an ML model was evaluated using an external database in 8 (67%) studies [ 30 - 34 , 37 , 38 ], single-institutional data in 3 (25%) studies [ 35 , 36 , 40 ], and data from 2 institutions in 2 (17%) studies [ 29 , 39 ]. No institutional data sets were published.…”

Section: Resultsmentioning

confidence: 99%

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Huang¹,

Galal²,

Etemadi³

et al. 2022

JMIR Med Inform

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Background Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear. Objective Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML. Methods A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included. Results Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors’ chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them. Conclusions The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.

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Section: Resultsmentioning

confidence: 99%

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Huang¹,

Galal²,

Etemadi³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…Other studies have taken different approaches to mitigate bias in clinical machine learning models, including a bias correction method based on learning curve fitting and removing the features that exacerbate bias. 38 , 39 However, in our case, we worked with a pragmatic dataset that had a fixed sample size, and our LIME experiments did not reveal major disparities in CUIs between racial subgroups. Our recalibration approach is an average approach across all predicted probabilities but methods in decision curve analysis may be applied to better delineate differing threshold probabilities that are better suited for each subgroup.…”

Section: Discussionmentioning

confidence: 94%

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

Thompson

Sharma

Bhalla

et al. 2021

Journal of the American Medical Informatics Association

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Objectives To assess fairness and bias of a previously validated machine learning opioid misuse classifier. Materials & Methods Two experiments were conducted with the classifier’s original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier’s predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics. Results We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included “heroin” and “substance abuse” across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). Discussion The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed. Conclusion Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.

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“…One strategy is to adjust models according to empirically observed patterns of bias, such as a recalibration methodology, which have been previously proposed as a potential method to reduce bias and minimize, in particular, decisions related to the overtreatment of healthy individuals 5,34 . Another potential approach is to reweight existing models 36,40,41 within each subgroup of the population, resulting in distinct weights for each subgroup of interest. Yet another strategy is to create new larger models that include certain variables (e.g., socioeconomic deprivation) 35,5 that may offer more consistent prognostic value across subgroups, as well as variables defined to greater precision (e.g., more precise quantification of self-reported race(s)).…”

Section: Discussionmentioning

confidence: 99%

Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk Assessment

Kartoun¹,

Khurshid

Kwon³

et al. 2021

Preprint

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Prediction models are commonly used to estimate risk for cardiovascular diseases; however, performance may vary substantially across relevant subgroups of the population. Here we investigated the variability of performance and fairness across a variety of subgroups for risk prediction of two common diseases, atherosclerotic cardiovascular disease (ASCVD) and atrial fibrillation (AF). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large data sets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity of established cardiovascular risk scores across subpopulations defined by age, sex, and presence of preexisting disease. For example, in CHARGE-AF, discrimination declined with increasing age, with concordance index of 0.72 [ 95% CI, 0.72–0.73 ] for the youngest (45–54y) subgroup to 0.57 [ 0.56–0.58 ], for the oldest (85–90y) subgroup in Explorys. The statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65–74y subgroup with a value of -0.33 [ 95% CI, -0.33–-0.33 ]. We observed also that large segments of the population suffered from both decreased discrimination (i.e., <0.7) and poor calibration (i.e., calibration slope outside of 0.7–1.3); for example, all individuals 75 or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify how clinical risk models behave and perform within specific subpopulations so they can be used appropriately to facilitate more accurate and equitable assessment of disease risk.

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Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression

Cited by 69 publications

References 31 publications

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk Assessment

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