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    Using conversation topics for predicting therapy outcomes in schizophrenia. 
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    Using conversation topics for predicting therapy outcomes in schizophrenia.

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    Published version (627.4Kb)
    Volume
    6
    Pagination
    39 - 50
    DOI
    10.4137/BII.S11661
    Journal
    Biomed Inform Insights
    Issue
    Suppl 1
    ISSN
    1178-2226
    Metadata
    Show full item record
    Abstract
    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.
    Authors
    Howes, C; Purver, M; McCabe, R
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/6920
    Collections
    • Centre for Primary Care and Public Health [1462]
    Language
    eng
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