Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters
Open accessAbstract
Machine learning is being utilized at an increasing rate in clinical psychology. Applying machine learning comes with a number of challenges, both in deciding which algorithms to test, and how to evaluate the predictive performance. We focus on this last component, demonstrating across both a simulation and empirical example that the method researchers choose to evaluate prediction with machine learning can have large consequences for the substantive conclusions. More specifically, we demonstrate that one method of evaluation that has been used repeatedly in clinical research, the optimism corrected bootstrap, can result in extremely biased results when paired with specific machine learning algorithms. We conclude with providing recommendations for researchers and a discussion of additional best practices.
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Potential wrongful inflation of accuracy estimates (e.g. related to small sample sizes, absence of testing model performance in an external sample, and problematic validation methods) precludes reliable immediate implementation in clinical practice.
“potential wrongful inflation of accuracy estimates (e.g. related to small sample sizes, absence of testing model performance in an external sample, and problematic validation methods), precluding reliable immediate implementation in clinical practice (Hosseini et al., 2020; Jacobucci, Littlefield, Millner, Kleiman, & Steinley, 2020; Poldrack, Huckins, & Varoquaux, 2020).”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Jacobucci et al., 2020
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- Authors
- Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D.
- Affiliations
- University of Notre Dame (US)
- Texas Tech University (US)
- University of Missouri (US)
- Publisher
- Center for Open Science
- Published
- 2020-02-07
- OpenAlex
- W3026966711
- Cited by
- 12
- References
- 46
- Citations by year
- 2026: 1 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 1 · 2019: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- machine learning, clinical psychology, predictive performance evaluation, evaluation bias, simulation study, algorithm selection, best practices
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