# Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder

## TL;DR

This study identifies a small set of language features related to social skills that can reliably distinguish between healthy controls, schizophrenia, and bipolar disorder using automated analysis, with high accuracy and validity.

## Contribution

The paper introduces a validated, small feature set for automated language analysis that effectively classifies mental disorders, reducing overfitting risks associated with larger feature sets.

## Key findings

- Language features strongly correlate with social skill assessments (r=0.75).
- Classifier achieves high accuracy in distinguishing clinical groups (AUC=0.96).
- Effective differentiation between schizophrenia and bipolar disorder (AUC=0.83).

## Abstract

Several studies have shown that speech and language features, automatically extracted from clinical interviews or spontaneous discourse, have diagnostic value for mental disorders such as schizophrenia and bipolar disorder. They typically make use of a large feature set to train a classifier for distinguishing between two groups of interest, i.e. a clinical and control group. However, a purely data-driven approach runs the risk of overfitting to a particular data set, especially when sample sizes are limited. Here, we first down-select the set of language features to a small subset that is related to a well-validated test of functional ability, the Social Skills Performance Assessment (SSPA). This helps establish the concurrent validity of the selected features. We use only these features to train a simple classifier to distinguish between groups of interest. Linear regression reveals that a subset of language features can effectively model the SSPA, with a correlation coefficient of 0.75. Furthermore, the same feature set can be used to build a strong binary classifier to distinguish between healthy controls and a clinical group (AUC = 0.96) and also between patients within the clinical group with schizophrenia and bipolar I disorder (AUC = 0.83).