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

背景(考古学) 患者体验 医学 定性研究 比例(比率) 医疗保健 心理学 医学教育 护理部 古生物学 经济 社会学 物理 生物 量子力学 经济增长 社会科学
作者
Kelly R. Voigt,Yingtao Sun,Ayush Patandin,J.H.C.L. Hendriks,Richard Goossens,Cornelis Verhoef,Olga Husson,Dirk J. Grünhagen,Jiwon Jung
出处
期刊:JMIR cancer [JMIR Publications]
卷期号:11: e58834-e58834 被引量:2
标识
DOI:10.2196/58834
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
英姑应助娴娴超爱笑采纳,获得10
刚刚
1秒前
科研通AI6.2应助pp采纳,获得25
2秒前
水蜜桃发布了新的文献求助10
2秒前
4秒前
5秒前
6秒前
满意时光完成签到,获得积分10
7秒前
小二郎应助照相机采纳,获得10
7秒前
8秒前
稀饭完成签到,获得积分10
8秒前
cultromics发布了新的文献求助10
8秒前
科研通AI6.2应助筚路蓝缕采纳,获得10
9秒前
yjp790403发布了新的文献求助10
10秒前
10秒前
12秒前
水蜜桃完成签到,获得积分10
13秒前
15秒前
冷山完成签到,获得积分10
15秒前
野枳完成签到,获得积分10
18秒前
yjp790403完成签到,获得积分10
19秒前
19秒前
liu发布了新的文献求助10
19秒前
Tong发布了新的文献求助10
20秒前
23秒前
能干的易形完成签到,获得积分10
23秒前
李思松完成签到 ,获得积分10
23秒前
23秒前
24秒前
24秒前
24秒前
wp完成签到 ,获得积分10
27秒前
顺利的钢笔发布了新的文献求助200
28秒前
JamesPei应助无名小羊采纳,获得10
28秒前
illidana发布了新的文献求助10
28秒前
Kimin完成签到,获得积分10
29秒前
zhuzhu发布了新的文献求助10
29秒前
鬆是松发布了新的文献求助10
30秒前
30秒前
积极的邪欢关注了科研通微信公众号
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
HYDROLYSE ACIDE DE QUELQUES DIOXASPIROCYCLANES 1314
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Navigating Normative Orders. Interdisciplinary Perspectives 800
1 Peter and Christ's Descent to the Dead in Its Early Christian Reception 700
Organizational Behavior 510
Management and the Arts 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7747950
求助须知:如何正确求助?哪些是违规求助? 9296180
关于积分的说明 20233931
捐赠科研通 7329325
什么是DOI,文献DOI怎么找? 3308744
关于科研通互助平台的介绍 2460530
邀请新用户注册赠送积分活动 2320713