Herb-Drug Interactions: A Holistic Decision Support System in Healthcare
This addresses a critical safety issue for patients and healthcare professionals by improving the detection of herb-drug interactions, though it appears incremental as it builds on existing decision support systems with machine learning enhancements.
The paper tackles the problem of herb-drug interactions in healthcare by designing a hybrid decision support system that uses machine learning to identify new possible interactions, aiming to assist pharmacists in making more accurate therapeutic decisions and mitigating adverse events.
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.