Predicting the risk of psychosis onset: advances and prospects

Abstract

Aim: To conduct a systematic review of the methods and performance characteristics of models developed for predicting the onset of psychosis.

Methods: We performed a comprehensive literature search restricted to English articles and identified using PubMed, Medline and PsychINFO, as well as the reference lists of published studies and reviews. Inclusion criteria included the selection of more than one variable to predict psychosis or schizophrenia onset, and selection of individuals at familial risk or clinical high risk. Eighteen studies met these criteria, and we compared these studies based on the subjects selected, predictor variables used and the choice of statistical or machine learning methods.

Results: Quality of life and life functioning as well as structural brain imaging emerged as the most promising predictors of psychosis onset, particularly when they were coupled with appropriate dimensionality reduction methods and predictive model algorithms like the support vector machine (SVM). Balanced accuracy ranged from 100% to 78% in four studies using the SVM, and 67% to 81% in 14 studies using general linear models.

Conclusions: Performance of the predictive models improves with quality of life measures, life functioning measures, structural brain imaging data, as well as with the use of methods like SVM. Despite these advances, the overall performance of psychosis predictive models is still modest. In the future, performance can potentially be improved by including genetic variant and new functional imaging data in addition to the predictors that are used currently.

Figure 1

Intuitive description of the SVM. (a) In the two-dimensional case, the separating linear decision boundary is a line. Although many lines can be drawn to separate the two sets of points, the SVM constructs support vectors (represented as the dotted lines) to choose the hyperplane that represents the largest distance from the two sets. (b) An outlier lies in the space of the wrong group and thus prevents the construction of a linear decision boundary. The SVM can deal with this problem by a user-specified “soft margin” parameter which controls the number of examples that are allowed to violate a decision boundary (c) The SVM brings the data points into a higher-dimensional space through a mathematical transformation called a “kernel”, where the extra dimension is the squared distance from the origin in this case. In this new three-dimensional space, the points are linear separable by a hyperplane, even though these points are not linearly separable in two-dimensional space.