Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.

ABSTRACT:

Background

Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.

Methods

The review was conducted following PRISMA-ScR guidance. Six databases (MEDLINE, EMBASE, Web of Science, IEEE Xplore, PubMed and SCOPUS) were searched for relevant records published before 2/2/2024. Search terms related to the concepts "artificial intelligence", "prediction

SUBMITTER: Moglia V 

PROVIDER: S-EPMC11773903 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature