A Personalized Data-to-Text Support Tool for Cancer Patients

Saar Hommes, Chris van der Lee, Felix Clouth, Jeroen Vermunt, Xander Verbeek, Emiel Krahmer


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.
Anthology ID:
W19-8656
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
443–452
Language:
URL:
https://aclanthology.org/W19-8656
DOI:
10.18653/v1/W19-8656
Bibkey:
Cite (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.
Cite (Informal):
A Personalized Data-to-Text Support Tool for Cancer Patients (Hommes et al., INLG 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8656.pdf