The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Overfitting to ‘predict’ suicidal ideation
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Claims other papers make about this one
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A recent work demonstrated that a high-profile article predicting suicidal ideation in youth had no predictive power after removing feature selection leakage.
“For instance, a recent work36 demonstrated that a high-profile article predicting suicidal ideation in youth had no predictive power after removing feature selection leakage.”
From Data leakage inflates prediction performance in connectome-based machine learning models (2024) · cites this paper as [36]
Citations
Cites
1 retracted
Just, M. A., Pan, L., Cherkassky, V. L., McMakin, D. L., Cha, C., Nock, M. K., & Brent, D. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0234-y · Cited by 210
RetractedCircular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 884
Subjectivity and Intersubjectivity in Psychiatric Diagnosis
Fuchs, T. A. E. (2010) · Psychopathology · doi:10.1159/000315126 · Cited by 133
Systematic misestimation of machine learning performance in neuroimaging studies of depression
Flint, C., Cearns, M., Opel, N., Redlich, R., Mehler, D. M. A., Emden, D., Winter, N. R., Leenings, R., Eickhoff, S. B., Kircher, T., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01020-7 · Cited by 120
Flagged first, then by citations
Research works printed but not linked: 1
- Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).
Cited by
Sorted by citations
Data leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46150-w · Cited by 236
Metadata
- Authors
- Verstynen, T., & Kording, K. P.
- Affiliations
- Carnegie Mellon University (US)
- University of Pennsylvania (US)
- Corresponding author
- Verstynen, Timothy
- Venue
- Nature Human Behaviour
- Published
- 2023-04-06
- Volume
- 7
- Issue
- 5
- Pages
- 680-681
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4362673421
- Cited by
- 15
- References
- 5
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 2
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- suicidal ideation, prediction, overfitting, machine learning, suicide risk prediction, model generalization
- MeSH terms
- Humans, Risk Factors, Suicide, Suicidal Ideation
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