Ngema Francis, Mdhluli Bonginkosi, Mmileng Pako, Shungube Precious, Makgaba Mokgoropo, Hossana Twinomurinzi
Centre of Applied Data Science, University of Johannesburg, Johannesburg, South Africa.
Front Res Metr Anal. 2024 Nov 19;9:1493944. doi: 10.3389/frma.2024.1493944. eCollection 2024.
Cervical cancer represents a significant public health challenge, particularly affecting women's health globally. This study aims to advance the understanding of cervical cancer risk prediction research through a bibliometric analysis. The study identified 800 records from Scopus and Web of Science databases, which were reduced to 142 unique records after removing duplicates. Out of 100 abstracts assessed, 42 were excluded based on specific criteria, resulting in 58 studies included in the bibliometric review. Multiple scoping methods such as thematic analysis, citation analysis, bibliographic coupling, natural language processing, Latent Dirichlet Allocation and other visualisation techniques were used to analyse related publications between 2013 and 2024. The key findings revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction, integrating expertise from mathematical disciplines, biomedical health, healthcare practitioners, public health, and policy. This approach significantly enhanced the accuracy and efficiency of cervical cancer detection and predictive modelling by adopting advanced machine learning algorithms, such as random forests and support vector machines. The main challenges were the lack of external validation on independent datasets and the need to address model interpretability to ensure healthcare providers understand and trust the predictive models. The study revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction. It made recommendations for future research to focus on increasing the external validation of models, improving model interpretability, and promoting global research collaborations to enhance the comprehensiveness and applicability of cervical cancer risk prediction models.
宫颈癌是一项重大的公共卫生挑战，尤其对全球女性健康产生影响。本研究旨在通过文献计量分析增进对宫颈癌风险预测研究的理解。该研究从Scopus和Web of Science数据库中识别出800条记录，去除重复记录后缩减至142条唯一记录。在评估的100篇摘要中，根据特定标准排除了42篇，最终有58项研究纳入文献计量综述。采用了多种范围界定方法，如主题分析、引文分析、文献耦合、自然语言处理、潜在狄利克雷分配以及其他可视化技术，来分析2013年至2024年间的相关出版物。主要研究结果揭示了跨学科合作在宫颈癌风险预测中的重要性，整合了数学学科、生物医学健康、医疗从业者、公共卫生和政策等方面的专业知识。通过采用随机森林和支持向量机等先进的机器学习算法，这种方法显著提高了宫颈癌检测和预测模型的准确性和效率。主要挑战在于缺乏对独立数据集的外部验证，以及需要解决模型可解释性问题，以确保医疗服务提供者理解并信任预测模型。该研究揭示了跨学科合作在宫颈癌风险预测中的重要性。它为未来研究提出了建议，即专注于增加模型的外部验证、提高模型可解释性，并促进全球研究合作，以增强宫颈癌风险预测模型的全面性和适用性。
