Abstract
Fairness in terms of various sensitive or protected attributes such as race, gender, age group, etc. has been a subject of great importance in the healthcare domain. Group fairness is considered as one of the principal criteria. However, most of the prevailing mitigation techniques emphasize on tuning the training algorithms while overlooking the fact that the training data may possibly be the primary reason for the biased outcomes. In this work, we address two sensitive attributes (age group and gender) with empirical evaluations of systemic inflammatory response syndrome (SIRS) classification for a dataset extracted from electronic health records (EHRs) for the essential task of improving equity in outcomes. Machine learning (ML)-based technologies are progressively becoming prevalent in hospitals; therefore, our approach carries out a demand for the frameworks to consider performance trade-offs regarding sensitive patient attributes combined with model training and permit organizations to utilize their ML resources in manners that are aware of potential fairness and equity issues. With the intended purpose of fairness, we experiment with a number of strategies to reduce disparities in algorithmic performance with respect to gender and age group. We leverage a sample and label balancing technique using weighted loss along with adversarial learning for an observational cohort derived from EHRs to introduce a “fair” SIRS classification model with minimized discrepancy in error rates over different groups. We experimentally illustrate that our strategy has the ability to align the distribution of SIRS classification outcomes for the models constructed from high-dimensional EHR data across a number of groups simultaneously.
ELISE STUDY GROUP: Louisa Bode\(^{\;a}\); Marcel Mast\(^{\;a}\); Antje Wulff\(^{\;a ,\; d}\); Michael Marschollek\(^{\;a}\); Sven Schamer\(^{\;b}\); Henning Rathert\(^{\;b}\); Thomas Jack\(^{\;b}\); Philipp Beerbaum\(^{\;b}\); Nicole Rübsamen\(^{\;c}\); Julia Böhnke\(^{\;c}\); André Karch\(^{\;c}\); Pronaya Prosun Das\(^{\;e}\); Lena Wiese\(^{\;e}\); Christian Groszweski-Anders\(^{\;f}\); Andreas Haller\(^{\;f}\); Torsten Frank\(^{\;f}\)
\(^{a}\)Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany.
\(^{b}\)Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Hannover, Germany.
\(^{c}\)Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany.
\(^{d}\)Big Data in Medicine, Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.
\(^{e}\)Research Group Bioinformatics, Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany.
\(^{f}\)medisite GmbH, Hannover, Germany.
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Acknowledgment
The ELISE project is partially funded by the Federal Ministry of Health; Grant No. 2520DAT66A. This work was also partially supported by the Fraunhofer Internal Programs under Grant No. Attract 042-601000. Ethics approval for use of routine data was given by the Ethics Committee of Hannover Medical School (approval number 9819_BO_S_2021). We would like to thank our colleagues from the MHH Information Technology (MIT) from the Hannover Medical School for their support.
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Das, P.P., Mast, M., Wiese, L., Jack, T., Wulff, A., ELISE STUDY GROUP. (2024). Algorithmic Fairness in Healthcare Data with Weighted Loss and Adversarial Learning. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_18
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