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

Research output: Contribution to conferencePaperScientificpeer-review

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.
Original languageEnglish
Publication statusPublished - 2019
Event12th International conference on Natural Language Generation (INLG 2019) - Tokyo, Japan
Duration: 29 Oct 20191 Nov 2019
https://www.inlg2019.com

Conference

Conference12th International conference on Natural Language Generation (INLG 2019)
CountryJapan
CityTokyo
Period29/10/191/11/19
Internet address

Fingerprint

Decision making

Keywords

  • Shared decision making
  • Natural Language Generation
  • Colorectal cancer

Cite this

Hommes, S., van der Lee, C., Clouth, F., Vermunt, J., Verbeek, X., & Krahmer, E. (2019). A Personalized Data-to-Text Support Tool for Cancer Patients. Paper presented at 12th International conference on Natural Language Generation (INLG 2019), Tokyo, Japan.
Hommes, Saar ; van der Lee, Chris ; Clouth, Felix ; Vermunt, Jeroen ; Verbeek, Xander ; Krahmer, Emiel. / A Personalized Data-to-Text Support Tool for Cancer Patients. Paper presented at 12th International conference on Natural Language Generation (INLG 2019), Tokyo, Japan.
@conference{c617a4c2b9ae4691a3fe929464833df3,
title = "A Personalized Data-to-Text Support Tool for Cancer Patients",
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.",
keywords = "Shared decision making, Natural Language Generation, Colorectal cancer",
author = "Saar Hommes and {van der Lee}, Chris and Felix Clouth and Jeroen Vermunt and Xander Verbeek and Emiel Krahmer",
year = "2019",
language = "English",
note = "12th International conference on Natural Language Generation (INLG 2019) ; Conference date: 29-10-2019 Through 01-11-2019",
url = "https://www.inlg2019.com",

}

Hommes, S, van der Lee, C, Clouth, F, Vermunt, J, Verbeek, X & Krahmer, E 2019, 'A Personalized Data-to-Text Support Tool for Cancer Patients', Paper presented at 12th International conference on Natural Language Generation (INLG 2019), Tokyo, Japan, 29/10/19 - 1/11/19.

A Personalized Data-to-Text Support Tool for Cancer Patients. / Hommes, Saar; van der Lee, Chris; Clouth, Felix; Vermunt, Jeroen; Verbeek, Xander; Krahmer, Emiel.

2019. Paper presented at 12th International conference on Natural Language Generation (INLG 2019), Tokyo, Japan.

Research output: Contribution to conferencePaperScientificpeer-review

TY - CONF

T1 - A Personalized Data-to-Text Support Tool for Cancer Patients

AU - Hommes, Saar

AU - van der Lee, Chris

AU - Clouth, Felix

AU - Vermunt, Jeroen

AU - Verbeek, Xander

AU - Krahmer, Emiel

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Shared decision making

KW - Natural Language Generation

KW - Colorectal cancer

M3 - Paper

ER -

Hommes S, van der Lee C, Clouth F, Vermunt J, Verbeek X, Krahmer E. A Personalized Data-to-Text Support Tool for Cancer Patients. 2019. Paper presented at 12th International conference on Natural Language Generation (INLG 2019), Tokyo, Japan.