从在线药物评论洞察女性避孕产品的副作用:基于自然语言处理的内容分析

JMIR AI Pub Date : 2025-04-03 DOI:10.2196/68809
Nicole Groene, Audrey Nickel, Amanda E Rohn
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引用次数: 0

摘要

背景:大多数在线和社交媒体上关于女性避孕方法的讨论都集中在副作用上,强调了分享这些产品经验的需求。在线用户对节育产品的评论和评级提供了一个很大程度上尚未开发的补充资源,可以帮助妇女及其伴侣做出知情的避孕选择。目的:本研究旨在分析女性对各种避孕方法的在线评分和评论,重点关注与低产品评分相关的副作用。方法:采用自然语言处理(NLP)进行主题建模和描述性统计,对drugs.com网站上发布的19506条女性避孕产品的独立评论进行分析。具有高系统吸收的激素避孕药,如单孕激素避孕药和延长周期避孕药,比其他方法得到更多的负面评价,妇女经常描述月经不规律,持续出血和体重增加与服用相关。宫内节育器总体上得到了更积极的评价,尽管大约十分之一的使用者报告了严重的痉挛和疼痛,这与非常低的评价有关。结论:虽然是探索性的,但这项研究强调了NLP在分析广泛的在线评论方面的潜力,以揭示女性使用避孕药的经历以及副作用对其整体健康的影响。除了来自临床研究的结果,nlp从在线评论中获得的见解可以为女性和卫生保健提供者提供补充信息,尽管在线评论可能存在偏见。研究结果表明,需要进一步的研究来验证特定副作用、避孕方法和女性整体健康之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insights on the Side Effects of Female Contraceptive Products From Online Drug Reviews: Natural Language Processing-Based Content Analysis.

Background: Most online and social media discussions about birth control methods for women center on side effects, highlighting a demand for shared experiences with these products. Online user reviews and ratings of birth control products offer a largely untapped supplementary resource that could assist women and their partners in making informed contraception choices.

Objective: This study sought to analyze women's online ratings and reviews of various birth control methods, focusing on side effects linked to low product ratings.

Methods: Using natural language processing (NLP) for topic modeling and descriptive statistics, this study analyzes 19,506 unique reviews of female contraceptive products posted on the website Drugs.com.

Results: Ratings vary widely across contraception types. Hormonal contraceptives with high systemic absorption, such as progestin-only pills and extended-cycle pills, received more unfavorable reviews than other methods and women frequently described menstrual irregularities, continuous bleeding, and weight gain associated with their administration. Intrauterine devices were generally rated more positively, although about 1 in 10 users reported severe cramps and pain, which were linked to very poor ratings.

Conclusions: While exploratory, this study highlights the potential of NLP in analyzing extensive online reviews to reveal insights into women's experiences with contraceptives and the impact of side effects on their overall well-being. In addition to results from clinical studies, NLP-derived insights from online reviews can provide complementary information for women and health care providers, despite possible biases in online reviews. The findings suggest a need for further research to validate links between specific side effects, contraceptive methods, and women's overall well-being.

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