Lee Young Ji, Park Albert, Roberge Mary, Donovan Heidi
Author Affiliations: School of Nursing and (Drs Lee and Donovan and Ms Roberge); Department of Biomedical Informatics (Dr Lee), University of Pittsburgh, Pennsylvania; College of Computing and Information Science, University of North Carolina, Charlotte (Dr Park); Department of Obstetrics, Gynecology and Reproductive Science, University of Pittsburgh, Pennsylvania (Dr Donovan).
Cancer Nurs. 2022;45(1):E27-E35. doi: 10.1097/NCC.0000000000000860.
Ovarian cancer (OvCa) patients suffer from symptoms that severely affect quality of life. To optimally manage these symptoms, their symptom experiences must be better understood. Social media have emerged as a data source to understand these experiences.
The objective of this study was to use topic modeling (ie, latent Dirichlet allocation [LDA]) to understand the symptom experience of OvCa patients through analysis of online forum posts from OvCa patients and their caregivers.
INTERVENTIONS/METHODS: Ovarian cancer patient/caregiver posts (n = 50 626) were collected from an online OvCa forum. We developed a symptom dictionary to identify symptoms described therein, selected the top 5 most frequently discussed symptoms, extracted posts that mentioned at least one of those symptoms, and conducted LDA on those extracted posts.
Pain, nausea, anxiety, fatigue, and skin rash were the top 5 most frequently discussed symptoms (n = 4536, 1296, 967, 878, and 657, respectively). Using LDA, we identified 11 topic categories, which differed across symptoms. For example, chemotherapy-related adverse effects likely reflected fatigue, nausea, and rash; social and spiritual support likely reflected anxiety; and diagnosis and treatment often reflected pain.
The frequency of a symptom discussed on a social media platform may not include all symptom experience and their severity. Indeed, users, who are experiencing different symptoms, mentioned different topics on the forum. Subsequent studies should consider the influence of additional factors (eg, cancer stage) from discussions.
Social media have the potential to prioritize and answer the questions about clinical care that are frequently asked by cancer patients and their caregivers.
卵巢癌（OvCa）患者会出现严重影响生活质量的症状。为了优化这些症状的管理，必须更好地了解他们的症状体验。社交媒体已成为了解这些体验的数据源。
本研究的目的是通过分析卵巢癌患者及其护理人员在在线论坛上的帖子，使用主题建模（即潜在狄利克雷分配 [LDA]）来了解卵巢癌患者的症状体验。
干预措施/方法：从一个在线卵巢癌论坛收集了卵巢癌患者/护理人员的帖子（n = 50626）。我们开发了一个症状词典来识别其中描述的症状，选择了讨论最频繁的前5种症状，提取了提及至少其中一种症状的帖子，并对这些提取的帖子进行了LDA分析。
疼痛、恶心、焦虑、疲劳和皮疹是讨论最频繁的前5种症状（分别为n = 4536、1296、967、878和657）。使用LDA，我们确定了11个主题类别，这些类别因症状而异。例如，化疗相关的不良反应可能反映了疲劳、恶心和皮疹；社会和精神支持可能反映了焦虑；而诊断和治疗通常反映了疼痛。
在社交媒体平台上讨论的症状频率可能不包括所有症状体验及其严重程度。事实上，经历不同症状的用户在论坛上提到了不同的主题。后续研究应考虑讨论中其他因素（如癌症分期）的影响。
社交媒体有潜力对癌症患者及其护理人员经常问到的有关临床护理的问题进行优先排序并给出答案。
