Using automated metaphor identification to aid in detection and prediction of first-episode schizophrenia

Abstract

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes many years and cannot be easily formalized into objective measures. However, clinical research suggests there are disturbances in aspects of the language use of patients with schizophrenia, which opens a door for the use of NLP tools in schizophrenia diagnosis and prognosis. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.