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Types of Knowledge Required to Personalise Smoking Cessation Letters

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Artificial Intelligence in Medicine (AIMDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1620))

Abstract

The STOP system generates personalised smoking-cessation letters, using as input responses to a smoking questionnaire. Generating personalised patient-information material is an area of growing interest to the medical community, since for many people changing health-related behaviour is the most effective possible medical intervention. While previous AI systems that generated personalised patient-information material were primarily based on medical knowledge, STOP is largely based on knowledge of psychology, empathy, and readability. We believe such knowledge is essential in systems whose goal is to change people's behaviour or mental state; but there are many open questions about how this knowledge should be acquired, represented, and reasoned with.

Many thanks to the experts who worked with us, including Scott Lennox, James Friend, Martin Pucci, Margaret Taylor, and Chris Bushe; and also to Scott Lennox and Jim Hunter for their comments on earlier drafts of this paper. This research was supported by the Scottish Office Department of Health under grant K/OPR/2/2/D318, and the Engineering and Physical Sciences Research Council under grant GR/L48812.

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Author information

Authors and Affiliations

  1. Dept. of Computing Science, University of Aberdeen, Aberdeen, Scotland

    Ehud Reiter & Roma Robertson

  2. Dept. of Medicine and Therapeutics, University of Aberdeen, Aberdeen, Scotland

    Liesl Osman

Authors
  1. Ehud Reiter
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  2. Roma Robertson
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  3. Liesl Osman
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Editor information

Editors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Werner Horn

  2. Department of Medicine and Computer Science, Stanford University, 251 Campus Drive, Stanford, CA, 94305-5479, USA

    Yuval Shahar

  3. Department of Medicine Karolinska Institutet, Huddinge University Hospital, Sweden

    Greger Lindberg

  4. Department of Medical Informatics and Image Analysis, Aalborg University, Fredrik Bajers Vej 7D, DK-9000, Aalborg, Denmark

    Steen Andreassen

  5. School of Public Policy, University College London, 29/30 Tavistock Square, London, WC1H 9EZ, UK

    Jeremy Wyatt

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© 1999 Springer-Verlag Berlin Heidelberg

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Reiter, E., Robertson, R., Osman, L. (1999). Types of Knowledge Required to Personalise Smoking Cessation Letters. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_43

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  • DOI: https://doi.org/10.1007/3-540-48720-4_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66162-7

  • Online ISBN: 978-3-540-48720-3

  • eBook Packages: Springer Book Archive

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