Summary of Ethical Considerations Of Use Of Hold-out Sets in Clinical Prediction Model Management, by Louis Chislett et al.
Ethical considerations of use of hold-out sets in clinical prediction model management
by Louis Chislett, Louis JM Aslett, Alisha R Davies, Catalina A Vallejos, James Liley
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Clinical prediction models are statistical or machine learning models that quantify patient risk based on data, informing potential interventions that can influence the outcome. The performative prediction effect occurs when predictions inform interventions that alter the predicted outcome. To mitigate this issue, researchers suggest using hold-out sets, where a subset of patients doesn’t receive model-derived risk scores, allowing for safe retraining. This paper explores ethical considerations regarding hold-out set implementation in healthcare settings, focusing on beneficence, non-maleficence, autonomy, and justice principles. The authors discuss informed consent, clinical equipoise, truth-telling, and statistical issues arising from different sampling methods. They also highlight the differences between hold-out sets and randomized control trials in terms of ethics and statistics. Finally, the paper provides practical recommendations for researchers interested in using hold-out sets for clinical prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Clinical prediction models are used to predict patient risk based on data. However, this can cause a problem called performative prediction where predictions change the outcome they were trying to predict. One way to fix this is by not giving some patients their predicted risk score. This paper looks at the ethics of doing this in hospitals and clinics. It talks about important principles like making sure patients are safe, not hurting them, letting them make their own choices, and being fair. The authors also discuss what it means to get informed consent from patients, when it’s okay to do a study without asking every patient, and how to be honest with patients. They even give examples of how this could work in different situations. Finally, they offer advice for researchers who want to use this method. |
Keywords
» Artificial intelligence » Machine learning
