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Janssen RJ, Mourão-Miranda J, Schnack HG. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(9):798-808doi: 10.1016/j.bpsc.2018.04.004.
Janssen, R. J., Mourão-Miranda, J., & Schnack, H. G. (2018). Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological psychiatry. Cognitive neuroscience and neuroimaging, 3(9), 798-808. https://doi.org/10.1016/j.bpsc.2018.04.004
Janssen, Ronald J, et al. "Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning." Biological psychiatry. Cognitive neuroscience and neuroimaging vol. 3,9 (2018): 798-808. doi: https://doi.org/10.1016/j.bpsc.2018.04.004
Janssen RJ, Mourão-Miranda J, Schnack HG. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Sep;3(9):798-808. doi: 10.1016/j.bpsc.2018.04.004. Epub 2018 Apr 22. PMID: 29789268.
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Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Sep;3(9):798-808. doi: 10.1016/j.bpsc.2018.04.004. Epub 2018 Apr 22.

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Biological psychiatry. Cognitive neuroscience and neuroimaging

Ronald J Janssen, Janaina Mourão-Miranda, Hugo G Schnack

Affiliations

  1. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands. Electronic address: [email protected].
  2. Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
  3. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

PMID: 29789268 DOI: 10.1016/j.bpsc.2018.04.004

Abstract

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers.

Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Keywords: Imaging; Machine learning; Major depressive disorder; Prediction; Prognosis; Schizophrenia

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