Suppr超能文献

绘制初级卫生保健中癌症研究的知识结构和研究热点:基于机器学习的分析

Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

作者信息

Damar Muhammet, Turhan Damar Hale, Özbiçakci Şeyda, Yasli Gökben, Erenay Fatih Safa, Özdağoğlu Güzin, Pinto Andrew David

机构信息

Dokuz Eylul University, İzmir, Türkiye.

Upstream Lab, MAP, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada.

出版信息

Medicine (Baltimore). 2025 Mar 21;104(12):e41749. doi: 10.1097/MD.0000000000041749.

Abstract

In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehensively evaluates cancer studies from the perspective of PHC using bibliometric techniques and machine learning. The dataset for the analyses was sourced from the Web of Science (WoS) Core Collection database on March 20, 2024. The Bibliometrix package within the R programming environment, alongside the Biblioshiny application, and VOSViewer software were employed for the bibliometric analyses. In this study, Latent Dirichlet Allocation was utilized as a prominent topic modeling algorithm. The implementation of this technique utilized Python along with the SciKit-Learn and Gensim libraries, ensuring robust model development and evaluation. The 2040 articles were produced by a total of 6705 different authors, 2166 different affiliations, and 75 different countries. Cancer survivors are more vulnerable and need more sensitive health services. The most intensively studied 3 cancer types in the PHC, listed by prevalence, are colorectal cancer, breast cancer, and cervical cancer. Additionally, prominent research topics in PHC include cancer screening, diagnosis, early detection, prevention, education, genetic factors and family history, risk factors, symptoms/signs, preventive medicine, referral and consultation, chronic disease management and health services research for cancer patients, health care disparities, palliative care, and communication with patients in PHC. Family physicians, being the first point of contact with the public, play a crucial role in preventing cancer cases, caring for patients with active cancer diagnoses, supporting cancer survivors in their post-cancer lives, and identifying and referring cancer cases at the earliest stages. However, cancer has many types, each with its own distinct symptoms, as well as similar types to each other. At this point, periodic educational training for doctors on cancer by health authorities, regular publication of cancer-related guidance resources by the central healthcare system, development of integrated decision support tools used by physicians during patient care, and the creation of informative mobile applications for cancer prevention or post-cancer life for patients have been considered highly critical.

摘要

在当代抗癌斗争中,初级卫生保健(PHC)服务在医疗保健系统中占据着重要且关键的地位。本研究作为对初级保健领域癌症研究最详尽的调查之一,运用文献计量学技术和机器学习,从初级卫生保健的视角全面评估癌症研究。分析数据集于2024年3月20日源自科学网(WoS)核心合集数据库。R编程环境中的Bibliometrix包、Biblioshiny应用程序以及VOSViewer软件用于文献计量分析。在本研究中,潜在狄利克雷分配被用作一种突出的主题建模算法。该技术的实施使用了Python以及SciKit - Learn和Gensim库,确保了强大的模型开发和评估。这2040篇文章由总共6705位不同作者、2166个不同机构以及75个不同国家产出。癌症幸存者更为脆弱,需要更贴心的卫生服务。按患病率列出的初级卫生保健中研究最深入的3种癌症类型是结直肠癌、乳腺癌和宫颈癌。此外,初级卫生保健中的突出研究主题包括癌症筛查、诊断、早期检测、预防、教育、遗传因素和家族病史、风险因素、症状/体征、预防医学、转诊和会诊、癌症患者的慢性病管理以及卫生服务研究、医疗保健差异、姑息治疗以及初级卫生保健中与患者的沟通。家庭医生作为与公众的第一接触点,在预防癌症病例、照顾确诊患有癌症的患者、支持癌症幸存者的癌症后生活以及尽早识别和转诊癌症病例方面发挥着关键作用。然而,癌症有多种类型,每种都有其独特症状,且彼此之间也有相似类型。至此,卫生当局定期为医生开展癌症教育培训、中央医疗保健系统定期发布癌症相关指导资源、开发医生在患者护理期间使用的综合决策支持工具以及为患者创建用于癌症预防或癌症后生活的信息丰富的移动应用程序,都被认为至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a516/11936571/d33a8b0c3506/medi-104-e41749-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验