Accurate and Safe Drug Recommendations based on Singular Value Decomposition

Polypharmacy is the prescription of many drugs for a critically-ill patient, which may have a higher risk of adverse drug reactions among them. Such patients usually follow a treatment that consists of multiple different drugs, which increases the risk of unwanted side-effects and can be occasionally harmful. In this paper, we address the safe drug prescription issues, by integrating the patient's Electronic Health Records (EHRs) with an adversarial Drug-Drug Interaction (DDI) knowledge graph. We study the task of predicting drugs to patients by making use of the real life data set MIMIC III. Our goal is to predict the next drug combination for a patient's therapy and at the same time minimize the drug unwanted side effects. We feed the input data to 6 different algorithms that we later compare in terms of effectiveness and safety in recommending drug combinations. Furthermore, we use the Post Hoc Re-rank technique together with Singular Value Decomposition algorithm to incorporate information in our prediction model from unwanted drug-drug interactions knowledge graph. Our experiments have shown that with a slight loss in the efficacy of the recommendation algorithm, we are able to reduce the toxicity score of the suggested drug combinations.