Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review

TL;DR

This paper reviews methods for handling temporal data in Electronic Health Records to improve detection of Adverse Drug Events, highlighting current approaches, challenges, and the potential of EHRs for pharmacovigilance.

Contribution

It categorizes existing methods for temporal data handling in EHRs for ADE detection and discusses challenges and opportunities in this research area.

Findings

Five main approaches identified: temporal abstraction, graph-based, weighted learning, time series data tables.

EHRs are a valuable source for automatic ADE detection.

Significant challenges remain in exploiting heterogeneous temporal data.

Abstract

The increased adoption of Electronic Health Records(EHRs) has brought changes to the way the patient care is carried out. The rich heterogeneous and temporal data space stored in EHRs can be leveraged by machine learning models to capture the underlying information and make clinically relevant predictions. This can be exploited to support public health activities such as pharmacovigilance and specifically mitigate the public health issue of Adverse Drug Events(ADEs). The aim of this article is, therefore, to investigate the various ways of handling temporal data for the purpose of detecting ADEs. Based on a review of the existing literature, 11 articles from the last 10 years were chosen to be studied. According to the literature retrieved the main methods were found to fall into 5 different approaches: based on temporal abstraction, graph-based, learning weights and data tables containing time series of different length. To that end, EHRs are a valuable source that has led current research to the automatic detection of ADEs. Yet there still exists a great deal of challenges that concerns the exploitation of the heterogeneous, data types with temporal information included in EHRs for predicting ADEs.