@inproceedings{hommes-etal-2019-personalized,
title = "A Personalized Data-to-Text Support Tool for Cancer Patients",
author = "Hommes, Saar and
van der Lee, Chris and
Clouth, Felix and
Vermunt, Jeroen and
Verbeek, Xander and
Krahmer, Emiel",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "–" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8656/",
doi = "10.18653/v1/W19-8656",
pages = "443--452",
abstract = "In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In our work, we rely on a newly developed prediction model, which assigns patients to scenarios. Furthermore, we use data-to-text techniques to explain these scenario-based predictions in personalized and understandable language. We highlight the possibilities of NLG for personalization, discuss ethical implications and also present the outcomes of a first evaluation with clinicians."
}
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<abstract>In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In our work, we rely on a newly developed prediction model, which assigns patients to scenarios. Furthermore, we use data-to-text techniques to explain these scenario-based predictions in personalized and understandable language. We highlight the possibilities of NLG for personalization, discuss ethical implications and also present the outcomes of a first evaluation with clinicians.</abstract>
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%0 Conference Proceedings
%T A Personalized Data-to-Text Support Tool for Cancer Patients
%A Hommes, Saar
%A van der Lee, Chris
%A Clouth, Felix
%A Vermunt, Jeroen
%A Verbeek, Xander
%A Krahmer, Emiel
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F hommes-etal-2019-personalized
%X In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In our work, we rely on a newly developed prediction model, which assigns patients to scenarios. Furthermore, we use data-to-text techniques to explain these scenario-based predictions in personalized and understandable language. We highlight the possibilities of NLG for personalization, discuss ethical implications and also present the outcomes of a first evaluation with clinicians.
%R 10.18653/v1/W19-8656
%U https://aclanthology.org/W19-8656/
%U https://doi.org/10.18653/v1/W19-8656
%P 443-452
Markdown (Informal)
[A Personalized Data-to-Text Support Tool for Cancer Patients](https://aclanthology.org/W19-8656/) (Hommes et al., INLG 2019)
ACL
- Saar Hommes, Chris van der Lee, Felix Clouth, Jeroen Vermunt, Xander Verbeek, and Emiel Krahmer. 2019. A Personalized Data-to-Text Support Tool for Cancer Patients. In Proceedings of the 12th International Conference on Natural Language Generation, pages 443–452, Tokyo, Japan. Association for Computational Linguistics.