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Towards increasing the clinical applicability of machine learning biomarkers in psychiatry

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The Original Article was published on 30 October 2017

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Fig. 1: Effects of parameter choices on the accuracy for differentiation between suicide ideators and controls.

References

  1. Egger, H. L. et al. Test–retest reliability of the Preschool Age Psychiatric Assessment (PAPA). J. Am. Acad. Child Adolesc. Psychiatry 45, 538–549 (2006).

    Article  Google Scholar 

  2. Hyman, S. E. The diagnosis of mental disorders: the problem of reification. Annu. Rev. Clin. Psychol. 6, 155–179 (2010).

    Article  Google Scholar 

  3. Bzdok, D. & Meyer-Lindenberg, A. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).

    PubMed  Google Scholar 

  4. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article  CAS  Google Scholar 

  5. Just, M. A. et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat. Hum. Behav. 1, 911–919 (2017).

    Article  Google Scholar 

  6. Cui, Z. & Gong, G. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. NeuroImage 178, 622–637 (2018).

    Article  Google Scholar 

  7. Varoquaux, G. Cross-validation failure: small sample sizes lead to large error bars. NeuroImage 180, 68–77 (2018).

    Article  Google Scholar 

Download references

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J.D. and S.B.E. designed the work and wrote the manuscript. S.G. and S.W. contributed to interpretation and substantively revised the manuscript.

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Correspondence to Simon B. Eickhoff.

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Dukart, J., Weis, S., Genon, S. et al. Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat Hum Behav 5, 431–432 (2021). https://doi.org/10.1038/s41562-021-01085-w

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