Two-day appointment scheduling with patient preferences and geometric arrivals

We consider an appointment system where the patients have preferences about the appointment days. A patient may be scheduled on one of the days that is acceptable to her, or be denied appointment. The patient may or may not show up at the appointed time. The net cost is a convex function of the actual number of patients served on a given day. We study the optimal scheduling policy that minimizes the long-run average cost and study its structural properties. We advocate an index policy, which is easy to implement, performs well in comparison with other heuristic policies, and is close to the optimal policy.     

Analysis of financial stock markets through multidimensional scaling based on information measures

In this paper, a novel multidimensional scaling (MDS) based on information measures method is proposed to analyze financial stock markets. In order to examine the effectiveness of this method, we applied it to the classification of two types of artificial series, the logistic map model and the cubic map model, as well as stock time series. Moreover, the traditional MDS using Euclidean dissimilarity is also provided as a reference for comparisons. The results show that the MDS based on information measures can give us more detailed, exact and clearer information on the classification of simulation series and stock time series than the MDS using Euclidean dissimilarity. In addition, the proposed graphical method may also assist in the construction of multivariate econometric models.     

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.