Deep Reinforcement Learning for Medicine Recommendation

Medicine recommendation is denoted as the task of predicting drug combinations for patients' therapies with complex diseases (i.e., cancer, diabetes, etc.). These patients often follow a treatment that consists of multiple drugs simultaneously, focusing at different human targets such as genes, proteins, etc. Previous research has already integrated the patients' Electronic Health Records (EHRs) with an adversarial Drug-Drug Interaction (DDI) knowledge graph to predict the next drug combination for a patient's therapy and minimize the drug side effects. However, they miss to consider additional valuable information that comes from synergistic Drug-Drug interaction knowledge graphs. In this paper, we integrate an EHR graph, which incorporates the patient, the disease, the therapy, and the drug information, with a Synergistic and/or an Adversarial DDI knowledge graph to recommend both accurate and safe medication. By identifying those drugs which can act synergistically and/or adversely, we are able to improve either the efficacy of the patient's therapy or minimize the toxicity and drug side effects. We have run experiments with two real-life medical data sets. Our results show that we can assist doctors to prescribe effective and safe medication for the patients' treatment.