Multimodal Prediction of Psychosis in the Prospective MoBa Birth Cohort

TL;DR

The study explores using multiple data sources and machine learning to better predict psychosis in adolescents.

Contribution

The paper introduces a multimodal machine learning framework that improves psychosis prediction accuracy.

Findings

General mental health factors achieved the highest balanced accuracy in unimodal classification.

CAPE scores and multimodal models further improved prediction accuracy.

Multimodal models showed better performance than unimodal approaches.

Abstract

There is a need for improved early psychosis detection beyond the traditional clinical high-risk strategy. Using the Norwegian Mother, Father and Child cohort study, we examined the predictive ability of self-reported psychotic experiences (Community Assessment of Psychic Experiences; CAPE) at age 14, in addition to general mental health factors, parent and childhood psychiatric diagnoses, schizophrenia polygenic risk scores, and birth-related factors, to predict subsequent psychosis onset using three machine learning approaches for imbalanced data. We explored also a multimodal prediction framework. For unimodal classification, we observed best balanced accuracies with general mental health factors (67.27 ± 1.76%), and CAPE (65.95 ± 1.09%). Multimodal models improved classification accuracy (68.38 ± 2.16%). With validation and additional model refinement, these features may be useful for initial screening within clinical stepped assessment frameworks.

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