Machine learning in female urinary incontinence: A scoping review.

ABSTRACT:

Introduction and hypothesis

The aim was to conduct a scoping review of the literature on the use of machine learning (ML) in female urinary incontinence (UI) over the last decade.

Methods

A systematic search was performed among the Medline, Google Scholar, PubMed, and Web of Science databases using the following keywords: [Urinary incontinence] and [(Machine learning) or (Predict) or (Prediction model)]. Eligible studies were considered to have applied ML model to explore different management processes of female UI. Data analyzed included the field of application, type of ML, input variables, and results of model validation.

Results

A total of 798 papers were identified while 23 finally met the inclusion criteria. The vast majority of studies applied logistic regressi

SUBMITTER: Wang Q 

PROVIDER: S-EPMC11459541 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature