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Using Conversation Topics for Predicting Therapy Outcomes in Schizophrenia

Howes, C., Purver, M. & McCabe, R. ORCID: 0000-0003-2041-7383 (2013). Using Conversation Topics for Predicting Therapy Outcomes in Schizophrenia. Biomedical Informatics Insights, 6s1, pp. 39-50. doi: 10.4137/bii.s11661


Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation.

Publication Type: Article
Additional Information: © The Author(s), publisher and licensee Libertas Academica Ltd.This is an open access article published under the Creative Commons CC-BY-NC 3.0 license.
Publisher Keywords: topic modelling, LDA, doctor-patient communication
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Health & Psychological Sciences > Healthcare Services Research & Management
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

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