PreprintCited by 12Artificial Intelligence in HealthcareStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters

Open access
Jacobucci, Ross, Littlefield, Andrew K., Millner, Alex J., Kleiman, Evan, Steinley, Douglas·2020

Abstract

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.

Integrity signals

Registry

Crossref · 10.31234/osf.io/2yber

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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

Cites

Flagged first, then by citations

Cited by

Sorted by citations

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)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review