Herb-Drug Interactions: A Holistic Decision Support System in Healthcare

TL;DR

This paper presents a hybrid AI-based decision support system designed to identify herb-drug interactions, aiming to improve clinical decision-making and reduce adverse events in healthcare.

Contribution

It introduces an original system combining machine learning with rule-based methods to detect herb-drug interactions more effectively.

Findings

The system can identify new potential herb-drug interactions.

It enhances decision-making accuracy for healthcare professionals.

The approach integrates AI techniques with traditional rules.

Abstract

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events.