Ethical considerations of use of hold-out sets in clinical prediction model management

Chislett, Louis; Aslett, Louis J. M.; Davies, Alisha R.; Vallejos, Catalina A.; Liley, James

Abstract

Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.

Citation

Chislett, L., Aslett, L. J. M., Davies, A. R., Vallejos, C. A., & Liley, J. (2025). Ethical considerations of use of hold-out sets in clinical prediction model management. AI and Ethics, 5(3), 2435-2444. https://doi.org/10.1007/s43681-024-00561-z

• Journal Article Type: Article
• Acceptance Date: Aug 21, 2024
• Online Publication Date: Sep 10, 2024
• Publication Date: Jun 1, 2025
• Deposit Date: Sep 11, 2024
• Publicly Available Date: Sep 11, 2024
• Journal: AI and Ethics
• Electronic ISSN: 2730-5961
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 3
• Pages: 2435-2444
• DOI: https://doi.org/10.1007/s43681-024-00561-z
• Public URL: https://durham-repository.worktribe.com/output/2860725