Machine learning enables automated screening for systematic reviews and meta-analysis in urology.

ABSTRACT:

Purpose

To investigate and implement semiautomated screening for meta-analyses (MA) in urology under consideration of class imbalance.

Methods

Machine learning algorithms were trained on data from three MA with detailed information of the screening process. Different methods to account for class imbalance (Sampling (up- and downsampling, weighting and cost-sensitive learning), thresholding) were implemented in different machine learning (ML) algorithms (Random Forest, Logistic Regression with Elastic Net Regularization, Support Vector Machines). Models were optimized for sensitivity. Besides metrics such as specificity, receiver operating curves, total missed studies, and work saved over sampling were calculated.

Results

During training, models trained after downsampl

SUBMITTER: Menold HS 

PROVIDER: S-EPMC11236840 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature