Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.

ABSTRACT:

Background

Surgical site infections (SSIs) remain a major cause of health care-associated infections, and early prediction is essential for improving patient outcomes. Machine learning (ML) has shown potential for SSI prediction; however, clinical implementation requires models that are both accurate and explainable. Despite recent progress in explainable ML, its clinical application to SSI prediction remains limited.

Objective

This study aimed to map explainable ML models for SSI prediction from a clinical perspective and examine their use of structured and unstructured data across the dimensions of data, methodology, and explanation output.

Methods

We conducted a scoping review following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses

SUBMITTER: Sun R 

PROVIDER: S-EPMC13626550 | biostudies-literature | 2026 Sep

REPOSITORIES: biostudies-literature