Unplanned hospital readmissions raise health care costs and cause significant distress to patients. Hence, predicting which patients are at risk to be readmitted is of great interest. In this paper, we mine large amounts of administrative information from claim data, including patients demographics, dispensed drugs, medical or surgical procedures performed, and medical diagnosis, in order to predict readmission using supervised learning methods. Our objective is to gain knowledge about the predictive power of the available information. Our preliminary results on data from the provincial hospital system in Quebec illustrate the potential for this approach to reveal important information on factors that trigger hospital readmission. Our findings suggest that a substantial portion of readmissions is inherently hard to predict. Consequently, the use of the raw readmission rate as an indicator of the quality of provided care might not be appropriate.

In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. These data are often used to build models that explain relationships between the measured variables, and are eventually used for planning and control purposes. However, these measurements cannot always be exact, systems can change over time, and discovering these facts or fixing these problems is not always feasible. Therefore it is important to formally describe the degree to which the model can tolerate noise, in order to keep near optimal behavior. The problem of finding tolerance bounds has been the focus of many studies for Markov Decision Processes (MDPs) due to their usefulness in practical applications. In this paper, we consider Partially Observable MDPs (POMDPs), which is a more realistic extension of MDPs with a wider scope of applications. We address two types of perturbations in POMDP model parameters, namely additive and multiplicative, and provide theoretical bounds for the impact of these changes in the value function. Experimental results are provided to illustrate our POMDP perturbation analysis in practice.

High dimensionality of belief space in partially observable Markov decision processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. Previous studies have demonstrated that the dimensionality of a POMDP can eventually be reduced by transforming it into an equivalent predictive state representation (PSR). In this paper, we address the problem of finding an approximate and compact PSR model corresponding to a given POMDP model. We formulate this problem in an optimization framework. Our algorithm tries to minimize the potential error that missing some core tests may cause. We also present an empirical evaluation on benchmark problems, illustrating the performance of this approach.