Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.

ABSTRACT:

Background

Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.

Methods

PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the C

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10641971 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature