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DOI: 10.4230/OASIcs.Commit2Data.3
Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options

Authors: Emiel Krahmer, Felix Clouth, Saar Hommes, Ruben Vromans, Steffen Pauws, Jeroen Vermunt, Lonneke van de Poll-Franse, and Xander Verbeek

Published in: OASIcs, Volume 124, Commit2Data (2024)

Abstract
When a person is diagnosed with cancer, difficult decisions about treatments need to be made. In this chapter, we describe an interdisciplinary research project which aims to automatically generate personalized descriptions of treatment options for patients. We relied on two large databases provided by the Netherlands Comprehensive Cancer Organisation (IKNL): The Netherlands Cancer Registry and the PROFILES dataset. Combining these datasets allowed us to extract personalized information about treatment options for different types of cancer. In a next step we provided personalized context to these numbers, both in verbal statements and in narratives, with the aim to facilitate shared decision making about treatments. We discuss strengths and limitations of our approach, illustrate how it generalizes to other health domains, and reflect on the overall research project.
Cite as

Emiel Krahmer, Felix Clouth, Saar Hommes, Ruben Vromans, Steffen Pauws, Jeroen Vermunt, Lonneke van de Poll-Franse, and Xander Verbeek. Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{krahmer_et_al:OASIcs.Commit2Data.3,
  author =	{Krahmer, Emiel and Clouth, Felix and Hommes, Saar and Vromans, Ruben and Pauws, Steffen and Vermunt, Jeroen and van de Poll-Franse, Lonneke and Verbeek, Xander},
  title =	{{Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options}},
  booktitle =	{Commit2Data},
  pages =	{3:1--3:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-351-5},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{124},
  editor =	{Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.3},
  URN =		{urn:nbn:de:0030-drops-213609},
  doi =		{10.4230/OASIcs.Commit2Data.3},
  annote =	{Keywords: Oncology, Data-driven shared decision making, Latent class analysis, Risk communication, Narratives, Personalization}
}
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