Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions

Abstract

Here we propose an approach for developing a diagnosis system for mood disorders, such as depression and bipolar disorder, based on language analysis from speech and text. Our system is based on the Mood State Indicator algorithm (MSI) for real-time analysis of a patient's mental state. MSI is designed to give a quantitative measure of cognitive state based on axiological values and time orientation of lexical features. MSI's multi-layered analytic engine consists of multiple information processing modules to systematically retrieve, parse and process features of a patient's discourse. Gold standard clinical criteria will be used to match language analysis indicators to mood disorder diagnosis.