O9.4. PREDICTING SCHIZOPHRENIA: IDENTIFICATION OF MULTIMODAL MARKERS OF DISEASE THROUGH A MACHINE LEARNING APPROACH

ABSTRACT: Abstract

Background

Diagnosis of schizophrenia is based on a collection of symptoms which are heterogeneous from one patient to the other. Therefore, improving the reliability of this diagnosis is a currently unmet need. Schizophrenia risk is associated with genetic variation and with environmental factors potentially affecting neurodevelopment. Moreover, among the symptoms, cognitive abnormalities are heritable and predate its clinical onset. Multivariate techniques can leverage the high dimensionality of data in order to study the combined effect of multiple risk factors and symptoms on clinical predictions. The aim of the current study is therefore to assess the predictability of schizophrenia diagnosis applying machine learning techniques to an ensemble of genetic, early envir

SUBMITTER: Antonucci L 

PROVIDER: S-EPMC5887307 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature