Safe and Effective Recommendation of Drug Combinations based on Matrix Co-Factorization

Nowadays the recommender systems represent an essential component in a huge variety of applications, and their continuous and rapid spread has allowed them to reach relevant fields like that of the healthcare. In such a delicate environment, it is extremely important to take into account all the variables that could occur, as the main objective is to support the doctors in prescribing accurate and safe treatments to patients affected by complex diseases. To address the aforementioned challenge, we decided to extend the Matrix Co-Factorization (MCF) method so that to allow it to include additional auxiliary data. A Post Hoc Re-Ranking technique has also been implemented to penalise the drugs that occur frequently in the adversarial drug-drug interactions knowdledge graph, therefore, to reduce the number of drugs in the final recommendations that could be dangerous for the patients' health. Different experiments have been used to assess the performances of our extended MCF. The results accomplished by our proposed method have also been compared against state-of-the-art models (such as C2PF, and PCRL) with a real-life dataset (MIMIC III). Our experiments depict that our method outperforms sufficiently the other methods in terms of accuracy and safety.