AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment
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AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatmentAutor(es)
Data
2024-05-04Cita bibliográfica
Rodriguez-Rodriguez AM, De la Fuente-Costa M, Escalera-de la Riva M, Perez-Dominguez B, Paseiro-Ares G, Casaña J, Blanco-Diaz M. AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment. SSM Popul Health. 2024 May 4;26:101677.
Resumo
[Abstract]
Background: Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9-68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.
Objective: This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.
Methods: A YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.
Results: The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.
Conclusions: YouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.
Palabras chave
Cancer
DISCERN
Dendrogram
GQS
HeatMap
Incontinence
Information
JAMA
MQ-VET
PCA
PMAT
Quality
Surgery
UMAP
YouTube
t-SNE
DISCERN
Dendrogram
GQS
HeatMap
Incontinence
Information
JAMA
MQ-VET
PCA
PMAT
Quality
Surgery
UMAP
YouTube
t-SNE
Versión do editor
Dereitos
Creative Commons Attribution 4.0 International License (CC-BY 4.0)
ISSN
2352-8273