Polycystic ovary syndrome (PCOS) is a heterogenous condition that is estimated to effect up to 21% of reproductive aged people with ovaries. In previous work, a dataset of PCOS features was derived from approximately 100,000 PCOS subreddit users via machine learning. In this study, an exploration of treatment response within the PCOS subreddit was undertaken with the derived dataset. The treatment or symptom features in the dataset had sentiment labels indicating when a treatment was perceived to improve or worsen a condition or symptom. When different features were mentioned within two sentences of each other without conflicting sentiment, it could be assumed that they were related. This assumption allowed for a broad analysis of the perceived effect of popular treatments on the most frequently mentioned symptoms. In general, lifestyle changes and supplements were the most positively regarded, while contraceptives were frequently associated with considerable negative sentiment. For PCOS weight loss, unspecified dieting (RR 5.19, 95% CI 3.28-8.19, n = 99) and intermittent fasting (RR 33.50, 95% CI 8.54-131.34, n = 69) were the most successful interventions. Inositol was associated with a large range of favourable outcomes and was one of the few treatments associated with improved mental health [depression (RR 4.25, 95% CI 1.72-10.51, n = 21), anxiety (RR 5.83, 95% CI 2.76-12.35, n = 41) and mood issues (RR 25.00, 95% CI 3.65-171.10, n = 26)]. Combined oral contraceptive pills as a whole were strongly associated with adverse effects such as worsening depression (RR 0.06, 95% CI 0.02-0.25, n = 33), anxiety (RR 0.10, 95% CI 0.03-0.36, n = 23), fatigue (RR 0, n = 45) and low libido (RR 0.03, 95% CI 0.01-0.24, n = 30). However, combined contraceptives with anti-androgenic progestins were associated with more favourable experiences. This study demonstrates the utility of machine learning to derive measurable patient experience data from an internet forum. While patient experience data derived using machine learning is not a substitute for traditional clinical trials, it is useful for mass validation and hypothesis generation. This paper may serve as the first exploration into this category of clinical internet forum research.

Keywords: Endocrinology; Internet research; Machine learning; PCOS.

© 2025. The Author(s).

多囊卵巢综合症（PCOS）是一种异质性疾病，据估计影响了高达21%的育龄女性。在之前的工作中，通过机器学习从大约100,000名患有PCOS的Reddit用户的数据集中提取了PCOS特征数据集。在这项研究中，使用该数据集对PCOS subreddit中的治疗反应进行了探索性分析。数据集中包含有关治疗或症状的特征，并带有表示某治疗被感知为改善或恶化某种状况或症状的情绪标签。当不同的特征在两个句子之内提到并且没有相反情绪时，可以假设它们是相关的。这一假设允许了对流行治疗方法对最常提及的症状所产生影响进行广泛分析的一般情况而言，生活方式改变和补充剂最受好评，而避孕药则频繁与相当负面的情绪相关联。对于PCOS减肥来说，未指明的节食（相对风险RR 5.19，95%置信区间CI 3.28-8.19，n = 99）和间歇性禁食（RR 33.50，CI 8.54-131.34，n = 69）是最有效的干预措施。肌醇与一系列有利的结果相关联，并且是少数被认为能改善心理健康状况的治疗方法之一[抑郁症（RR 4.25，CI 1.72-10.51，n = 21），焦虑症（RR 5.83，CI 2.76-12.35，n = 41）和情绪问题（RR 25.00，CI 3.65-171.10，n = 26）。]总体而言，复方口服避孕药与不良影响强烈相关联，例如恶化抑郁（RR 0.06，CI 0.02-0.25，n = 33），焦虑（RR 0.10，CI 0.03-0.36，n = 23），疲劳（RR 0，n = 45）和性欲减退（RR 0.03，CI 0.01-0.24，n = 30）。然而，含有抗雄激素孕激素的复方避孕药则与更积极的经历相关联。这项研究表明了机器学习从互联网论坛中提取可测量患者体验数据的实用性。尽管使用机器学习获取的数据不能替代传统临床试验，但它对大规模验证和假设生成非常有用。本文可能作为此类临床互联网论坛研究的第一项探索性分析。

关键词：内分泌学；互联网研究；机器学习；PCOS。

© 2025. The Author(s).