A systematic review of predictive models for hospital-acquired pressure injury using machine learning.

ABSTRACT:

Aims and objectives

To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models.

Background

As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study.

Design

Systematic review.

Methods

Relevant artic

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC9912391 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature