A Machine Learning Analysis of Queries Related to Blepharoplasty

Aatin K. Dhanda, Christopher C. Tseng, Jeff Gao, Guy Talmor, B. Paskhover
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Abstract

Introduction: Blepharoplasty is a commonly performed cosmetic procedure which can help reduce age-related changes around the eyes. Like other surgical procedures, patient satisfaction is highly correlated with physician responsiveness to questions. As social media becomes more popular, patients are increasingly turning to online sources of information such as Realself.com, a website where patients can ask questions to verified physicians. We extracted and analyzed blepharoplasty-related questions on Realself to gain greater understanding of commonly asked pre- and postoperative questions. Materials and Methods: A Web crawler was used to gather the text for each question under the search term “eyelid surgery.” Questions were individually assigned by the authors to respective pre- or postoperative categories. Preoperative categories included eligibility for surgery, nonsurgical options, potential adverse effects, surgeon recommendations, surgical techniques and logistics, ability to pursue other surgeries following blepharoplasty, cost, and miscellaneous. Postoperative question categories included symptoms after surgery, appearance, behavior allowed/disallowed, options to revise surgery, and miscellaneous. Machine learning was then utilized to establish the most common pre- and postoperative questions. Results: 2009 blepharoplasty questions were extracted in total. A total of 60.93% of questions were preoperative related and 39.07% of questions were postoperative related. Preoperative questions were predominantly about eligibility for surgery (43.85%), while the majority of postoperative question were related to symptoms after surgery (17.42%) and appearance (10.80%). Machine learning analysis showed that most preoperative questions inquired about the possibility of correcting specific features, whereas most postoperative questions focused on resolving complications following surgery. Conclusion: As health information becomes more prevalent online, our results underscore the need for greater awareness about frequently asked questions to help surgeons preemptively address patient concerns about blepharoplasty. Our 10 most common questions can be used in the clinical setting as a patient educational handout to augment the preoperative experience.
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眼睑成形术相关查询的机器学习分析
简介:眼睑成形术是一种常用的美容手术,可以帮助减少与年龄有关的眼睛周围的变化。像其他外科手术一样,患者满意度与医生对问题的反应高度相关。随着社交媒体变得越来越流行,患者越来越多地转向在线信息来源,比如Realself.com。在这个网站上,患者可以向经过认证的医生提问。我们提取并分析了Realself上睑成形术相关的问题,以更好地了解常见的术前和术后问题。材料和方法:使用网络爬虫收集搜索词“眼睑手术”下的每个问题的文本。问题由作者分别分配到各自的术前或术后类别。术前分类包括手术资格、非手术选择、潜在不良反应、外科医生建议、手术技术和后勤、眼睑成形术后进行其他手术的能力、费用和杂项。术后问题类别包括手术后症状、外观、允许/不允许的行为、修改手术的选择和其他问题。然后利用机器学习来确定最常见的术前和术后问题。结果:共提取眼睑成形术问题2009个。术前相关问题占60.93%,术后相关问题占39.07%。术前问题主要是关于手术的资格(43.85%),术后问题主要是关于手术后的症状(17.42%)和外观(10.80%)。机器学习分析显示,大多数术前问题询问纠正特定特征的可能性,而大多数术后问题侧重于解决手术后并发症。结论:随着健康信息在网上变得越来越普遍,我们的研究结果强调有必要提高对常见问题的认识,以帮助外科医生先发制人地解决患者对眼睑成形术的担忧。我们的10个最常见的问题可以用于临床设置作为患者教育讲义,以增加术前经验。
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