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A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter

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 نشر من قبل Eric Clark Mr.
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
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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 Twitters 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.



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