[论文解读] A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter

Eric M. Clark, Ted A. James+5 more|arXiv (Cornell University)|May 25, 2018

Social Media in Health Education被引用 28

一句话总结

本研究利用监督式机器学习与自然语言处理技术，分析了530万条与乳腺癌相关的Twitter推文，识别出患者报告的经历与情感。研究发现，尽管治疗体验的讨论多呈积极态度，但关于医疗保健的讨论整体上呈负面情绪，主要源于对拟议政治改革下可能失去保险覆盖的担忧。

ABSTRACT

Background: Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indicators both during and post treatment. In prior work, [Crannell et. al.], we have studied an active cancer patient population on Twitter and compiled a set of tweets describing their experience with this disease. We refer to these online public testimonies as "Invisible Patient Reported Outcomes" (iPROs), because they carry relevant indicators, yet are difficult to capture by conventional means of self-report. Methods: Our present study aims to identify tweets related to the patient experience as an additional informative tool for monitoring public health. Using Twitter's public streaming API, we compiled over 5.3 million "breast cancer" related tweets spanning September 2016 until mid December 2017. We combined supervised machine learning methods with natural language processing to sift tweets relevant to breast cancer patient experiences. We analyzed a sample of 845 breast cancer patient and survivor accounts, responsible for over 48,000 posts. We investigated tweet content with a hedonometric sentiment analysis to quantitatively extract emotionally charged topics. Results: We found that positive experiences were shared regarding patient treatment, raising support, and spreading awareness. Further discussions related to healthcare were prevalent and largely negative focusing on fear of political legislation that could result in loss of coverage. Conclusions: Social media can provide a positive outlet for patients to discuss their needs and concerns regarding their healthcare coverage and treatment needs. Capturing iPROs from online communication can help inform healthcare professionals and lead to more connected and personalized treatment regimens.

研究动机与目标

• 开发一种基于机器学习与自然语言处理的自动化方法，用于识别社交媒体中与患者报告经历相关的内容。
• 量化与乳腺癌治疗及医疗保健认知相关的推文情感。
• 探讨社交媒体如何作为‘不可见的患者报告结局’（iPROs）的来源，用于医疗监测。
• 利用Twitter数据评估公众对医疗保健政策变革，特别是《美国医疗保健法》的态度。
• 展示社交媒体作为实时公共卫生监测与患者参与工具的潜力。

提出的方法

• 通过Twitter流媒体API在15个月内收集了530万条包含‘breast’和‘cancer’的公开推文。
• 应用带有词嵌入的卷积神经网络（CNN）对推文进行分类，以识别与患者经历的相关性。
• 基于845条人工标注的患者推文小样本数据集训练分类器，以识别语境相关的文本内容。
• 进行享乐主义情感分析，以量化患者叙述中的情感基调。
• 使用另一组7640万条仅含‘cancer’的推文数据，以提升上下文识别能力并减少噪声。
• 通过人工审查验证结果，并将模型性能与基于关键词的过滤方法进行对比。

实验结果

研究问题

• RQ1 如何利用机器学习与自然语言处理技术，自动识别Twitter上与乳腺癌相关的患者报告经历？
• RQ2 在讨论乳腺癌治疗与医疗可及性的推文中，主导的情感模式是什么？
• RQ3 医疗保健政策改革如何影响社交媒体上乳腺癌患者群体的公众情绪？
• RQ4 社交媒体在多大程度上可作为实时、以患者为中心的健康数据（iPROs）来源，用于公共卫生监测？
• RQ5 Twitter上患者关于治疗体验与系统性医疗保健问题的讨论，其情感倾向有何差异？

主要发现

• 基于CNN的分类器成功从大规模数据集中识别出845条相关患者经历的推文，证明了自动化内容过滤的可行性。
• 积极情感在讨论治疗、情感支持及意识宣传的推文中占主导地位。
• 负面情感主导了关于医疗保健的讨论，尤其集中在对《美国医疗保健法》下可能失去保险覆盖的担忧上。
• 在线乳腺癌社群对拟议的医疗改革表达了强烈反对，表明存在显著的政治与情感反应。
• 社交媒体是捕捉患者情绪与公共卫生认知的宝贵实时平台，这些信息往往被传统报告所遗漏。
• 本研究证实，社交媒体能够放大患者在医疗政策议题上的声音，从而影响政治话语与公众意识。