You, WenqiSimalatsar, AlenaWidmer, NicolasDe Micheli, Giovanni2012-10-022012-10-022012-10-02201210.1109/BIBMW.2012.6470292https://infoscience.epfl.ch/handle/20.500.14299/85858Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients’ health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients’ feature data and the doctor’s clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accu- racy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features’ data is also proposed in this paper.Decision support systemsupport vector machineRANSAC algorithmtext::conference output::conference proceedings::conference paper