In the intensive care units of the hospitals, the critically ill patients
follow complex treatments consisting of drug combinations to avoid
mortality and have fast recovery. However, the drug combinations
of a patient’s treatment may cause unwanted side effects. In this
paper, we apply Principal Component Analysis (PCA) over patients’
treatment medical data, so that we can identify similar clinical cases
to the target patient and the effective and safe drug combinations
that these patients received. Moreover, we employ from internet
drug databases side information regarding the clinical trials of new
drugs and their unwanted side effects they may have with other
drugs. Our goal is the reduction of the unwanted side effects of
the recommended drug combinations by replacing some drugs
with their safer substitutes. Our experimental results have shown
the effectiveness and safety of our PCA method in terms of drug
recommendations compared with classic algorithms such as SVD,
NMF and user-KNN.