Analysing Dialogue for Diagnosis and Prediction in Mental Health

author: Matthew Purver, School of Electronic Engineering and Computer Science, Queen Mary, University of London
published: Jan. 9, 2018,   recorded: December 2017,   views: 3805
released under terms of: Creative Commons Attribution (CC-BY)


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Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. This talk will present work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions. We will look at clinical dialogue between patient and doctor or therapist, in cases involving schizophrenia, depression and dementia; in each case, we find that diagnostic information and/or treatment outcomes are related to observable features of a patient's language and interaction with their conversational partner. We discuss the nature of these phenomena and the suitability and accuracy of NLP techniques for detecting them automatically.

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