医学研究中鲜有研究鉴定主题趋势，而此项研究可为我们提供某个领域重视哪些主题的洞察。我们评估了使用机器学习方法确定《妇科肿瘤学》中发表的最常见研究主题以及随时间如何变化的可行性。我们使用PubMed检索了1990年至2020年期间《妇科肿瘤学》发表的所有原始研究摘要。将摘要文本经过自然语言处理算法进行处理，并使用隐含狄利克雷分配(LDA)对其进行主题聚类，然后进行了手工标记。对主题进行了时间趋势的研究。 我们检索到12,586篇原始研究文章，其中11,217篇可用于后续分析。主题建模完成后选择了23个研究主题。基础科学遗传学、流行病学方法和化疗这些主题在该时期内增长最快，而术后预后、育龄期癌症管理和宫颈上皮增生则经历了最大的下降。对基础科学研究的兴趣相对稳定。此外，还对探究手术或医疗治疗方面的词语进行了审查。手术和医疗主题都呈增长趋势，手术主题的增长幅度更大，且占发表主题的比例更高。 主题建模(一种无监督机器学习技术)成功地用于识别研究主题趋势。应用这种技术提供了洞察，了解妇科肿瘤学领域如何重视其实践范畴的组成部分，并因此确定如何分配资助金、传播研究结果以及参与公共话语等方面的决策。 版权所有©2023 Elsevier Inc.。保留所有权利。

There is scant research identifying thematic trends within medical research. This work may provide insight into how a given field values certain topics. We assessed the feasibility of using a machine learning approach to determine the most common research themes published in Gynecologic Oncology over a thirty-year period and to subsequently evaluate how interest in these topics changed over time.We retrieved the abstracts of all original research published in Gynecologic Oncology from 1990 to 2020 using PubMed. Abstract text was processed through a natural language processing algorithm and clustered into topical themes using latent Dirichlet allocation (LDA) prior to manual labeling. Topics were investigated for temporal trends.We retrieved 12,586 original research articles, of which 11,217 were evaluable for subsequent analysis. Twenty-three research topics were selected at the completion of topic modeling. The topics of basic science genetics, epidemiologic methods, and chemotherapy experienced the greatest increase over the time period, while postoperative outcomes, reproductive age cancer management, and cervical dysplasia experienced the greatest decline. Interest in basic science research remained relatively constant. Topics were additionally reviewed for words indicative of either surgical or medical therapy. Both surgical and medical topics saw increasing interest, with surgical topics experiencing a greater increase and representing a higher proportion of published topics.Topic modeling, a type of unsupervised machine learning, was successfully used to identify trends in research themes. The application of this technique provided insight into how the field of gynecologic oncology values the components of its scope of practice and therefore how it may choose to allocate grant funding, disseminate research, and participate in the public discourse.Copyright © 2023 Elsevier Inc. All rights reserved.