Williams Christopher Y K, Li Rosia X, Luo Michael Y, Bance Manohar
School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Clin Otolaryngol. 2023 May;48(3):442-450. doi: 10.1111/coa.14037. Epub 2023 Jan 31.
There is a paucity of research examining patient experiences of cochlear implants. We sought to use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community.
Cross-sectional study of posts on the online Reddit r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Natural language processing using the BERTopic automated topic modelling technique was employed to cluster posts into semantically similar topics. Topic categorisation was manually validated by two independent reviewers and Cohen's kappa calculated to determine inter-rater reliability between machine vs human and human vs human categorisation.
We retrieved 987 posts from 588 unique Reddit users on the r/CochlearImplants forum. Posts were initially categorised by BERTopic into 16 different Topics, which were increased to 23 Topics following manual inspection. The most popular topics related to CI connectivity (n = 112), adults considering getting a CI (n = 107), surgery-related posts (n = 89) and day-to-day living with a CI (n = 85). Cohen's kappa among all posts was 0.62 (machine vs. human) and 0.72 (human vs. human), and among categorised posts was 0.85 (machine vs. human) and 0.84 (human vs. human).
This cross-sectional study of social media discussions among the online cochlear implant community identified common attitudes, experiences and concerns of patients living with, or seeking, a cochlear implant. Our validation of natural language processing methods to categorise topics shows that automated analysis of similar Otolaryngology-related content is a viable and accurate alternative to manual qualitative approaches.
关于人工耳蜗患者体验的研究较少。我们试图使用自然语言处理方法来探索在线人工耳蜗（CI）社区中患者的体验和担忧。
对2015年3月1日至2021年11月11日在线Reddit的r/CochlearImplants论坛上的帖子进行横断面研究。使用BERTopic自动主题建模技术进行自然语言处理，将帖子聚类为语义相似的主题。由两名独立评审员对手动验证主题分类，并计算科恩kappa系数，以确定机器与人类以及人类与人类分类之间的评分者间信度。
我们从r/CochlearImplants论坛上的588名Reddit用户中检索到987个帖子。帖子最初由BERTopic分类为16个不同主题，经过人工检查后增加到23个主题。最热门的主题与CI连接性（n = 112）、考虑植入CI的成年人（n = 107）、手术相关帖子（n = 89）以及CI的日常生活（n = 85）有关。所有帖子中科恩kappa系数在机器与人类之间为0.62，在人类与人类之间为0.72，在分类后的帖子中，机器与人类之间为0.85，人类与人类之间为0.84。
这项对在线人工耳蜗社区社交媒体讨论的横断面研究确定了植入或寻求人工耳蜗的患者的共同态度、体验和担忧。我们对自然语言处理方法进行主题分类的验证表明，对类似耳鼻喉科相关内容的自动分析是一种可行且准确的替代手动定性方法。
