Language as a Subtyping Tool and a Potential Predictor of Treatment Outcome in Depression: Using Large Language Models to Harvest the Predictive Power of Language Restricted; Files & ToC

Abstract

Major depressive disorder (MDD) is a highly debilitating condition. Early treatment optimization is crucial for a favorable prognosis, but reliably predicting who is most likely to benefit from which treatment remains a major challenge. One way to address the problem is through a better understanding of the heterogeneity in the disease. Previous research identified language use as a potential indicator of individual differences in depression, and recent technological advancements permit a more systematic approach to the use of language in this regard. In the current study, we demonstrate how large language models (LLMs) can be used to identify sub-types of depression in the early stages of treatment based on people’s natural speech productions. We introduce a computational technique for determining the relative similarity of two narratives by measuring how one narrative affects an LLM’s ability to predict sentences in another narrative when it is used as a context. The resulting narrative similarities were analyzed using hierarchical clustering to reveal three major subgroups of depression. Subsequent feature analyses indicated distinguishing semantic and syntactic properties of each cluster and predictions about future remission status. The findings demonstrate how AI models applied to the analysis of people’s natural speech can be used in subtyping and predicting treatment outcomes for depression. 

Table of Contents

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About this Master's Thesis

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School	Laney Graduate School
Department	Psychology
Subfield / Discipline	Clinical Psychology
Degree	M.A.
Submission	Master's Thesis
Language	English
Research Field	Artificial Intelligence Psychology, Clinical Language, General
关键词	depression outcome prognosis large language models disease heterogeneity
Committee Chair / Thesis Advisor	Phillip Wolff, Emory University
Committee Members	W. Edward Craighead, Emory University Lynne Nygaard, Emory University

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