Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals.

Autor: Cabanillas Silva P; Dedalus Healthcare, Antwerp, Belgium., Sun H; Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314001, China; Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, 314001, China. Electronic address: hongsun@zjxu.edu.cn., Rodriguez-Brazzarola P; Dedalus Healthcare, Antwerp, Belgium., Rezk M; Dedalus Healthcare, Antwerp, Belgium., Zhang X; Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314001, China; Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, 314001, China., Fliegenschmidt J; Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany., Hulde N; Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany., von Dossow V; Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany., Meesseman L; Dedalus Healthcare, Antwerp, Belgium., Depraetere K; Dedalus Healthcare, Antwerp, Belgium., Szymanowsky R; Dedalus Healthcare, Antwerp, Belgium., Stieg J; Dedalus Healthcare, Antwerp, Belgium., Dahlweid FM; Dedalus Healthcare, Antwerp, Belgium.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2024 Sep; Vol. 157, pp. 104692. Date of Electronic Publication: 2024 Jul 14.
DOI: 10.1016/j.jbi.2024.104692
Abstrakt: Background: An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models. We aim to generalize our findings by performing this investigation on multiple use cases at different hospitals.
Methods: First, we conduct a thorough analysis of the source data to find gender-based disparities. Secondly, we assess the model performance on different gender groups at different hospitals and on different use cases. Performance evaluation is quantified using the area under the receiver-operating characteristic curve (AUROC). Lastly, we investigate the clinical implications of these biases by analyzing the underdiagnosis and overdiagnosis rate, and the decision curve analysis (DCA). We also investigate the influence of model calibration on mitigating gender-related disparities in decision-making processes.
Results: Our data analysis reveals notable variations in incidence rates, AUROC, and over-diagnosis rates across different genders, hospitals and clinical use cases. However, it is also observed the underdiagnosis rate is consistently higher in the female population. In general, the female population exhibits lower incidence rates and the models perform worse when applied to this group. Furthermore, the decision curve analysis demonstrates there is no statistically significant difference between the model's clinical utility across gender groups within the interested range of thresholds.
Conclusion: The presence of gender bias within risk prediction models varies across different clinical use cases and healthcare institutions. Although inherent difference is observed between male and female populations at the data source level, this variance does not affect the parity of clinical utility. In conclusion, the evaluations conducted in this study highlight the significance of continuous monitoring of gender-based disparities in various perspectives for clinical risk prediction models.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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