A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer - Explorative Study

Aug 5, 2026

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Rising numbers of cancer survivors and clinician shortages limit access to cancer care, making health technologies important for sustaining optimal patient journeys. This study uses a machine learning–supported approach with data from patient forums to understand daily experiences during the colorectal cancer (CRC) journey. Forum posts are analyzed with topic modeling, then reviewed in home versus hospital contexts to build a patient community journey map evaluated by CRC doctors and quality-of-life experts.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Background

Objective

Methods

Results

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Introduction

EUR Research Information Portal A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer Published in: JMIR Cancer Publication status and date: Published: 27/01/2025 DOI (link to publisher): 10.2196/58834 Document Version Publisher's PDF, also known as Version of record Document License/Available under: CC BY Citation for the published version (APA): Voigt, K. , Sun, Y. , Patandin, A. , Hendriks, J. , Goossens, R. H. , Verhoef, C. , Husson, O. , Grünhagen, D. , & Jung, J. (2025) . A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study. JMIR Cancer, 11, Article e58834 . [https://doi.org/10.2196/58834](https://doi.org/10.2196/58834) Link to publication on the EUR Research Information Portal Terms and Conditions of Use Except as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission: • you may download, save and print a copy of this material for your personal use only; • you may share the EUR portal link to this material. In case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license. Take-down policy If you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website. JMIR CANCER Voigt et al Original Paper A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study | Kelly Voigt 1 , MD, PhD; Yingtao Sun2 , MSc; Ayush Patandin3 , MSc; Johanna Hendriks4 , MD, PhD; Richard Hendrik Goossens2 , PhD; Cornelis Verhoef1 , MD, PhD; Olga Husson 1,5 , PhD; Dirk Grünhagen 1* , MD, PhD; Jiwon Jung2,4* , PhD | | --- | | 1Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands 2Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
3Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, Netherlands 4Department of Surgery, Erasmus Medical Centre, Rotterdam, Netherlands
5Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
*these authors contributed equally
Corresponding Author:
Jiwon Jung, PhD Department of Surgery Erasmus Medical Centre Dr. Molewaterplein 40 Rotterdam, 3015 GD Netherlands
Phone: 31 010 704 0704
Email: [j](j.jung@erasmusmc.nl)[.jung@erasmusmc.nl](j.jung@erasmusmc.nl)
Abstract | | Background: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care . Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective: This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods: Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source . Topic modeling, as a part of machine

FAQs

What is the objective of this study?

To identify and assess patient experiences on a large scale using a machine learning–supported approach leveraging patient forum data.

How are forum posts analyzed in the study?

Topic modeling recognizes topic patterns, and researchers review the most relevant posts per topic, separating them into “home” and “hospital” contexts.

What major outcomes does the study report?

From 212,107 posts, the study produced 37 topics and 10 upper clusters, including clusters reflecting daily life activities and treatment understanding, and it constructs a patient community journey map based on these findings.

Aug 5, 2026

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