使用主题建模的机器学习方法来识别和评估结直肠癌患者的经历:探索性研究。

IF 3.3 Q2 ONCOLOGY
JMIR Cancer Pub Date : 2025-01-27 DOI:10.2196/58834
Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung
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引用次数: 0

摘要

背景:癌症幸存者人数的增加和卫生保健专业人员的短缺对癌症护理的可及性提出了挑战。卫生技术对于维持最佳的病人旅程是必要的。为了了解患者的日常生活,定性研究至关重要。然而,并非所有患者都希望与研究人员分享他们的故事。目的:本研究旨在利用来自患者论坛的数据,利用一种新颖的机器学习支持的方法,大规模识别和评估患者体验。方法:以美国癌症幸存者网络结直肠癌(CRC)患者论坛帖子为数据来源。主题建模作为机器学习的一部分,用于识别帖子中的主题模式。研究人员阅读了每个主题中最相关的50篇文章,并将其分为“家庭”和“医院”两类。根据患者的故事,绘制了一幅患者社区旅程图,以直观地说明我们的发现。儿童权利中心的医生和一名生活质量专家对已确定的病人体验和地图主题进行了评估。结果:在212107篇帖子中,产生了37个主题和10个上层聚类。主要集群包括“CRC患者的日常活动”(38,782,18.3%)和“了解治疗包括替代和辅助治疗”(31,577,14.9%)。与医院情境相比,与家庭情境相关的话题具有更多的情感内容。基于这些发现,我们构建了患者社区旅程图。结论:我们的研究强调了CRC患者的不同关注点和经验。家庭环境讨论中更多的情感内容强调了CRC在临床环境之外的个人影响。基于我们的研究,我们发现机器学习支持的方法是一个很有前途的解决方案来分析患者的经历。患者社区旅程地图的创新应用为患者日常生活中的挑战提供了独特的视角,这对于在适当的时候提供适当的支持至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.

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.

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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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