Fan Jungwei W, Wang Wanjing, Huang Ming, Liu Hongfang, Hooten W Michael
Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.
Front Digit Health. 2023 Apr 24;5:958338. doi: 10.3389/fdgth.2023.958338. eCollection 2023.
Chronic pain (CP) lasts for more than 3 months, causing prolonged physical and mental burdens to patients. According to the US Centers for Disease Control and Prevention, CP contributes to more than 500 billion US dollars yearly in direct medical cost plus the associated productivity loss. CP is complex in etiology and can occur anywhere in the body, making it difficult to treat and manage. There is a pressing need for research to better summarize the common health issues faced by consumers living with CP and their experience in accessing over-the-counter analgesics or therapeutic devices. Modern online shopping platforms offer a broad array of opportunities for the secondary use of consumer-generated data in CP research. In this study, we performed an exploratory data mining study that analyzed CP-related Amazon product reviews. Our descriptive analyses characterized the review language, the reviewed products, the representative topics, and the network of comorbidities mentioned in the reviews. The results indicated that most of the reviews were concise yet rich in terms of representing the various health issues faced by people with CP. Despite the noise in the online reviews, we see potential in leveraging the data to capture certain consumer-reported outcomes or to identify shortcomings of the available products.
慢性疼痛（CP）持续超过3个月，给患者带来长期的身心负担。根据美国疾病控制与预防中心的数据，慢性疼痛每年导致超过5000亿美元的直接医疗费用以及相关的生产力损失。慢性疼痛的病因复杂，可发生在身体的任何部位，这使得其治疗和管理变得困难。迫切需要开展研究，以更好地总结慢性疼痛患者面临的常见健康问题以及他们在使用非处方镇痛药或治疗设备方面的经历。现代在线购物平台为在慢性疼痛研究中二次利用消费者生成的数据提供了广泛的机会。在本研究中，我们进行了一项探索性数据挖掘研究，分析了与慢性疼痛相关的亚马逊产品评论。我们的描述性分析对评论语言、被评论产品、代表性主题以及评论中提到的共病网络进行了特征描述。结果表明，大多数评论虽简洁，但在反映慢性疼痛患者面临的各种健康问题方面内容丰富。尽管在线评论存在噪音，但我们认为利用这些数据来获取某些消费者报告的结果或识别现有产品的缺点具有潜力。
