Roccetti Marco, Marfia Gustavo, Salomoni Paola, Prandi Catia, Zagari Rocco Maurizio, Gningaye Kengni Faustine Linda, Bazzoli Franco, Montagnani Marco
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.
Department for Life Quality Studies, University of Bologna, Rimini, Italy.
JMIR Public Health Surveill. 2017 Aug 9;3(3):e51. doi: 10.2196/publichealth.7004.
Data concerning patients originates from a variety of sources on social media.
The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients' perspectives on a given medical prescription.
To shed light on patients' behavior and concerns, we focused on Crohn's disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn's disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen's kappa coefficient method.
The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen's kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts' opinion and the software tool.
We show how data posted on Facebook by Crohn's disease patients are a useful dataset to understand the patient's perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients' opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients' sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients' perspective on a specific medical therapy.
关于患者的数据来源于社交媒体上的各种渠道。
本研究的目的是展示如何运用从计算机科学、计量经济学、统计学、数据挖掘和社会学等不同领域借鉴而来的方法，来分析脸书数据，以探究患者对特定药物处方的看法。
为了深入了解患者的行为和担忧，我们聚焦于克罗恩病（一种慢性炎症性肠病）以及生物药物英夫利昔单抗的特定治疗。为了从大数据池中获取信息，我们分析了2011年10月至2015年8月期间的脸书帖子。我们从患有克罗恩病且正在接受或曾接受单克隆抗体药物英夫利昔单抗治疗的患者所发布的帖子中进行筛选。对所选帖子进行进一步的特征描述和情感分析。最后，由来自不同科研领域（如计算机科学与胃肠病学）的专家以及运行情感分析工具的软件系统进行人种志审查。将患者对英夫利昔单抗治疗的感受分为积极、中性或消极，并使用平方加权科恩kappa系数方法比较计算机科学专家、胃肠病学家和软件工具的结果。
首次自动筛选过程返回了56000条脸书帖子，其中261条表达了患者对英夫利昔单抗的看法。对这261条所选帖子的人种志分析得出了相似的结果，计算机科学专家和胃肠病学专家之间的评分者间一致性为87.3%（228/261），根据平方加权科恩kappa系数方法（w2K = 0.6470），这是一个实质性的一致性。计算机科学专家将36%、27%和37%的帖子分别归为积极、中性和消极感受，胃肠病学家则分别为38%、30%和32%。在专家意见和软件工具之间仅发现了轻微的一致性。
我们展示了克罗恩病患者在脸书上发布的数据是一个有用的数据集，有助于理解患者对英夫利昔单抗特定治疗的看法。从脸书页面获取的真实、不受医学影响的患者意见，能够被来自不同研究背景的专家轻松审查，并且在患者情感分类上有实质性的一致性。所描述的方法允许快速收集大量数据，这些数据能够被轻松分析，以深入了解患者对特定医学治疗的看法。
