Text mining in a literature review of abnormal uterine bleeding according to the FIGO classification

Abstract

Aim: Abnormal uterine bleeding, as proposed in 2007, is defined as unexpected uterine bleeding in women of reproductive age; the cause of the bleeding is categorized using the PALM-COEIN system. Identifying the diagnostic and treatment modalities for each cause might be intriguing. To summarize the options for abnormal uterine bleeding assessment, we employed text-mining analysis for each of its causes.

Methods: We analyzed abstracts based on PALM-COEIN from PubMed and Web of Science in March 2022. The literature was divided into categories; topics about the disorders were retrieved, and covalent network analysis was conducted to find information for evaluating abnormal uterine bleeding.

Results: Diagnostic approaches for PALM included histological and image analysis, including computerized tomography, magnetic resonance imaging, sonography, and hysteroscopy. The therapeutic approaches varied according to the cause. Diagnostic approaches for COEIN were mostly medical history interviews and blood sampling, and the therapeutic approaches for COEIN were ablation, hysteroscopy, and hormonal treatment. The PALM-COEIN classification co-occurrence search revealed each cause's diagnostic procedures, symptoms, and treatment procedures.

Conclusion: Our text-mining methodology revealed comprehensive insights, important study themes, and clinical trends for abnormal uterine bleeding. A tailored approach to medical realities is required for treating abnormal uterine bleeding properly.

Keywords: gynecology; literature; obstetrics; text mining; uterine bleeding.