# 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.

## Key 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.