A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study
Abstract BackgroundThe 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 pat...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-01-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2025/1/e58834 |
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| author | Kelly Voigt Yingtao Sun Ayush Patandin Johanna Hendriks Richard Hendrik Goossens Cornelis Verhoef Olga Husson Dirk Grünhagen Jiwon Jung |
| author_facet | Kelly Voigt Yingtao Sun Ayush Patandin Johanna Hendriks Richard Hendrik Goossens Cornelis Verhoef Olga Husson Dirk Grünhagen Jiwon Jung |
| author_sort | Kelly Voigt |
| collection | DOAJ |
| description |
Abstract
BackgroundThe 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.
ObjectiveThis study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
MethodsForum 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.
ResultsBased 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.
ConclusionsOur 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. |
| format | Article |
| id | doaj-art-c11f95263cff4c42a8d4e312d1a7f9b5 |
| institution | Kabale University |
| issn | 2369-1999 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cancer |
| spelling | doaj-art-c11f95263cff4c42a8d4e312d1a7f9b52025-02-03T20:34:04ZengJMIR PublicationsJMIR Cancer2369-19992025-01-0111e58834e5883410.2196/58834A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative StudyKelly Voigthttp://orcid.org/0000-0002-4592-9409Yingtao Sunhttp://orcid.org/0009-0004-6703-5192Ayush Patandinhttp://orcid.org/0009-0007-1063-5738Johanna Hendrikshttp://orcid.org/0009-0000-8167-9251Richard Hendrik Goossenshttp://orcid.org/0000-0002-8304-3916Cornelis Verhoefhttp://orcid.org/0000-0001-9980-8613Olga Hussonhttp://orcid.org/0000-0002-1387-8686Dirk Grünhagenhttp://orcid.org/0000-0001-8293-6002Jiwon Junghttp://orcid.org/0000-0002-0157-196X Abstract BackgroundThe 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. ObjectiveThis study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums. MethodsForum 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. ResultsBased 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. ConclusionsOur 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.https://cancer.jmir.org/2025/1/e58834 |
| spellingShingle | Kelly Voigt Yingtao Sun Ayush Patandin Johanna Hendriks Richard Hendrik Goossens Cornelis Verhoef Olga Husson Dirk Grünhagen Jiwon Jung A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study JMIR Cancer |
| title | A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study |
| title_full | A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study |
| title_fullStr | A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study |
| title_full_unstemmed | A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study |
| title_short | A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study |
| title_sort | machine learning approach using topic modeling to identify and assess experiences of patients with colorectal cancer explorative study |
| url | https://cancer.jmir.org/2025/1/e58834 |
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