Mortality Prediction and Safe Drug Recommendation for Critically-ill Patients

Panagiotis Symeonidis, Theodoros Kostoulas, Vasiliki Danilatou, Christos Andras, Stergios Chairistanidis

Abstract: Drug recommendation is denoted as the task of predicting drug combinations for patients' therapies with complex diseases (i.e., thrombosis, diabetes, etc.). These patients usually suffer from polypharmacy, and consequently various drug drug interactions. In this paper, we integrate 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. In particular, we integrate an EHR graph, which incorporates the patient, the disease, the therapy, and the drug information, with an Adversarial DDI knowledge graph to recommend both accurate and safe medication. We also predict mortality and the time to death of critically-ill patients, to identify clinically meaningful predictors (e.g., harmful drug combinations). By identifying those drugs which can act adversarially, 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 a real-life medical data set. Our results show that we can assist doctors to prescribe effective and safe medication for the patients' treatment.