Review of Time Domain Electronic Medical Record Taxonomies in the Application of Machine Learning

Abstract

Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring real-time health trends. This review focuses on the time-series data needed to construct complete EMRs by identifying paradigms that fall within the scope of the application of artificial intelligence (AI) based on the principles of translational medicine. (1) Background: The question addressed in this study is: What are the taxonomies present in the field of the application of machine learning on EMRs? (2) Methods: Scopus, Web of Science, and PubMed were searched for relevant records. The records were then filtered based on a PRISMA review process. The taxonomies were then identified after reviewing the selected documents; (3) Results: A total of five main topics were identified, and the subheadings are discussed in this review; (4) Conclusions: Each aspect of the medical data pipeline needs constant collaboration and update for the proposed solutions to be useful and adaptable in real-world scenarios.