Evidence map›Paper›PMID 39874195›Full record

ArticleJMIR cancer2025

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

Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

Abstract read
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Patient Journey Mapping in Mental Disorders: A Scoping Review.International journal of mental health nursing · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kelly VoigtDepartment of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands.ORCID 0000-0002-4592-9409
Yingtao SunFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID 0009-0004-6703-5192
Ayush PatandinFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, Netherlands.ORCID 0009-0007-1063-5738
Johanna HendriksDepartment of Surgery, Erasmus Medical Centre, Rotterdam, Netherlands.ORCID 0009-0000-8167-9251
Richard Hendrik GoossensFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-8304-3916
Cornelis VerhoefDepartment of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands.ORCID 0000-0001-9980-8613
Olga HussonDepartment of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands.ORCID 0000-0002-1387-8686
Dirk Grünhagen *Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands.ORCID 0000-0001-8293-6002
Jiwon Jung *Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-0157-196X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into "home" or "hospital" contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map. Results: Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included "Daily activities while living with CRC" (38,782, 18.3%) and "Understanding treatment including alternatives and adjuvant therapy" (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings. Conclusions: Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients' experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients' daily lives, which is essential for delivering appropriate support at the right moment.

Indexed as

Cancer SurvivorsColorectal NeoplasmsMachine LearningQuality of LifeFemaleHumansMaleQualitative ResearchAIartificial intelligencecancer carecancer survivorcolorectal cancerforummachine learningpatient experiencepatient journeypostquality of lifetopictopic modelingUnited States

Identifiers

PMID39874195
PMCPMC11790180

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.