The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.

ABSTRACT: Machine Learning (ML) and Deep Learning (DL) models show potential in surpassing traditional methods including generalised linear models for healthcare predictions, particularly with large, complex datasets. However, low interpretability hinders practical implementation. To address this, Explainable Artificial Intelligence (XAI) methods are proposed, but a comprehensive evaluation of their effectiveness is currently limited. The aim of this scoping review is to critically appraise the application of XAI methods in ML/DL models using Electronic Health Record (EHR) data. In accordance with PRISMA scoping review guidelines, the study searched PUBMED and OVID/MEDLINE (including EMBASE) for publications related to tabular EHR data that employed ML/DL models with XAI. Out of 3220 identified publ

SUBMITTER: Caterson J 

PROVIDER: S-EPMC11528818 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature