Dataset Information

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.

ABSTRACT: Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and groupspecific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from a neglected step of the machine learning pipeline.

SUBMITTER: Jeanselme V

PROVIDER: S-EPMC7614014 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.

Jeanselme Vincent V De-Arteaga Maria M Zhang Zhe Z Barrett Jessica J Tom Brian B

Proceedings of machine learning research 20220101

Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific i ...[more]

PMID: 36601036

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Project description:ObjectivesStatistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. We conducted a scoping review on algorithmic individual fairness (IF) to understand the current state of research in the metrics and methods developed to achieve IF and their applications in healthcare.Materials and methodsWe searched four databases: PubMed, ACM Digital Library, IEEE Xplore, and medRxiv for algorithmic IF metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and November 2024. We identified 2498 articles through database searches and seven additional articles, of which 32 articles were included in the review. Data from the selected articles were extracted, and the findings were synthesized.ResultsBased on the 32 articles in the review, we identified several themes, including philosophical underpinnings of fairness, IF metrics, mitigation methods for achieving IF, implications of achieving IF on group fairness and vice versa, and applications of IF in healthcare.DiscussionWe find that research of IF is still in their early stages, particularly in healthcare, as evidenced by the limited number of relevant articles published between 2013 and 2024. While healthcare applications of IF remain sparse, growth has been steady in number of publications since 2012. The limitations of group fairness further emphasize the need for alternative approaches like IF. However, IF itself is not without challenges, including subjective definitions of similarity and potential bias encoding from data-driven methods. These findings, coupled with the limitations of the review process, underscore the need for more comprehensive research on the evolution of IF metrics and definitions to advance this promising field.ConclusionWhile significant work has been done on algorithmic IF in recent years, the definition, use, and study of IF remain in their infancy, especially in healthcare. Future research is needed to comprehensively apply and evaluate IF in healthcare.

Project description:ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, setting, and participantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main outcomes and measuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and relevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

Dataset Information

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.

Publications

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.

Similar Datasets

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