Predicting adherence to treatment for schizophrenia from dialogue transcripts

  • Authors:

  • Christine Howes;Matthew Purver;Rose McCabe;Patrick G. T. Healey;Mary Lavelle

  • Affiliations:

  • Queen Mary University of London, London;Queen Mary University of London, London;Queen Mary University of London, London;Queen Mary University of London, London;Queen Mary University of London, London

  • Venue:
  • SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
  • Year:
  • 2012

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Abstract

Recent work on consultations between out-patients with schizophrenia and psychiatrists has shown that adherence to treatment can be predicted by patterns of repair -- specifically, the pro-activity of the patient in checking their understanding, i.e. patient clarification. Using machine learning techniques, we investigate whether this tendency can be predicted from high-level dialogue features, such as backchannels, overlap and each participant's proportion of talk. The results indicate that these features are not predictive of a patient's adherence to treatment or satisfaction with the communication, although they do have some association with symptoms. However, all these can be predicted if we allow features at the word level. These preliminary experiments indicate that patient adherence is predictable from dialogue transcripts, but further work is necessary to develop a meaningful, general and reliable feature set.