Unsupervised learning of pharmacokinetic responses

Pharmacokinetics (PK) is a branch of pharmacology dedicated to the study of the time course of drug concentrations, from absorption to excretion from the body. PK dynamic models are often based on homogeneous, multi-compartment assumptions, which allow to identify the PK parameters and further predict the time evolution of drug concentration for a given subject. One key characteristic of these time series is their high variability among patients, which may hamper their correct stratification. In the present work, we address this variability by estimating the PK parameters and simultaneously clustering the corresponding subjects using the time series. We propose an expectation maximization algorithm that clusters subjects based on their PK drug responses, in an unsupervised way, collapsing clusters that are closer than a given threshold. Experimental results show that the proposed algorithm converges fast and leads to meaningful results in synthetic and real scenarios.