Data Mining and Analytical Models to Predict and Identify Adverse Drug-drug Interactions

TL;DR

This paper reviews analytical models for predicting adverse drug-drug interactions, highlighting recent improvements, limitations, and future directions including advanced neural network approaches.

Contribution

It introduces and compares models like Label Propagation and Supervised Learning with DGI data, and discusses the potential of TM-RNN for better DDI prediction.

Findings

Improved DDI identification using label propagation and DGI data.

Case study demonstrating Random Forest effectiveness in psoriasis DDI prediction.

Discussion of TM-RNN as a future approach to address current limitations.

Abstract

The use of multiple drugs accounts for almost 30% of all hospital admission and is the 5th leading cause of death in America. Since over 30% of all adverse drug events (ADEs) are thought to be caused by drug-drug interactions (DDI), better identification and prediction of administration of known DDIs in primary and secondary care could reduce the number of patients seeking urgent care in hospitals, resulting in substantial savings for health systems worldwide along with better public health. However, current DDI prediction models are prone to confounding biases along with either inaccurate or a lack of access to longitudinal data from Electronic Health Records (EHR) and other drug information such as FDA Adverse Event Reporting System (FAERS) which continue to be the main barriers in measuring the prevalence of DDI and characterizing the phenomenon in medical care. In this review, analytical models including Label Propagation using drug side effect data and Supervised Learning DDI Prediction model using Drug-Gene interactions (DGIs) data are discussed. Improved identification of DDIs in both of these models compared to previous versions are highlighted while limitations that include bias, inaccuracy, and insufficient data are also assessed. A case study of Psoriasis DDI prediction by DGI data using Random Forest Classifier was studied. Transfer Matrix Recurrent Neural Networks (TM-RNN) that address the above limitations are discussed in future works.