Research Output

How to Talk to Strangers: generating medical reports for first time users

  We propose a novel approach for handling first-time
users in the context of automatic report generation from timeseries
data in the health domain. Handling first-time users is
a common problem for Natural Language Generation (NLG)
and interactive systems in general - the system cannot adapt
to users without prior interaction or user knowledge. In this
paper, we propose a novel framework for generating medical
reports for first-time users, using multi-objective optimisation
(MOO) to account for the preferences of multiple possible
user types, where the content preferences of potential users
are modelled as objective functions. Our proposed approach
outperforms two meaningful baselines in an evaluation with
prospective users, yielding large (= :79) and medium (= :46)
effect sizes respectively.

  • Date:

    10 November 2016

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/FUZZ-IEEE.2016.7737739

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Gkatzia, D., Rieser, V. & Lemon, O. (2016). How to Talk to Strangers: generating medical reports for first time users. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)doi:10.1109/FUZZ-IEEE.2016.7737739. ISBN 978-1-5090-0626-7, 978-1-5090-0625-0,

Authors

Copyright

© © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

Multi-objective evolutionary algorithm; automatically generated natural language medical reports;

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