Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

ABSTRACT:

Objective

To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.

Materials and methods

We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availabili

SUBMITTER: Payrovnaziri SN 

PROVIDER: S-EPMC7647281 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature