Cargando…
2660. Evaluating Risk for Bacterial Vaginosis Utilizing an Unsupervised Machine Learning Approach
BACKGROUND: Clustering methods using machine learning may be useful for identifying variables predicting clinical outcomes. Despite the need to better understand risk behaviors of Bacterial Vaginosis (BV), the most common cause of abnormal vaginal discharge linked to STI and HIV acquisition, machine...
Autores principales: | Rodriguez, Violeta, Pan, Yue, Salazar, Ana, Fonseca, Nicholas, Raccamarich, Patricia, Klatt, Nichole R, Weiss, Deborah Jones, Alcaide, Maria L L |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10677155/ http://dx.doi.org/10.1093/ofid/ofad500.2271 |
Ejemplares similares
-
2086. PrEP Knowledge and Use among Reproductive Age Women in Miami, Florida
por: Nogueira, Nicholas Fonseca, et al.
Publicado: (2022) -
PrEP awareness and use among reproductive age women in Miami, Florida
por: Nogueira, Nicholas Fonseca, et al.
Publicado: (2023) -
Infection with SARS-CoV-2 is associated with menstrual irregularities among women of reproductive age
por: Cherenack, Emily M., et al.
Publicado: (2022) -
A bio-behavioral intervention to decrease intravaginal practices and bacterial vaginosis among HIV infected Zambian women, a randomized pilot study
por: Alcaide, Maria L., et al.
Publicado: (2017) -
The Evolving Facets of Bacterial Vaginosis: Implications for HIV Transmission
por: McKinnon, Lyle R., et al.
Publicado: (2019)