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Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Janssen, RJ; Mourão-Miranda, J; Schnack, HG; (2018) Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging , 3 (9) pp. 798-808. 10.1016/j.bpsc.2018.04.004. Green open access

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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.

Type: Article
Title: Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.bpsc.2018.04.004
Publisher version: https://doi.org/10.1016/j.bpsc.2018.04.004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Imaging, Machine learning, Major depressive disorder, Prediction, Prognosis, Schizophrenia
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060626
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