BackgroundAppropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.MethodsThe sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction.ResultsThe prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works.ConclusionThis study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

A computation task running in distributed systems can be represented as a directed graph H(V, E) whose vertices and edges may fail with known probabilities. In this paper, we introduce a reliability measure, called the distributed task reliability, to model the reliability of such computation tasks. The distributed task reliability is defined as the probability that the task can be successfully executed. Due to the and-fork/and-join constraint, the traditional network reliability problem is a special case of the distributed task reliability problem, where the former is known to be NP-hard in general graphs. For two-terminal and-or series-parallel (AOSP) graphs, the distributed task reliability can be computed in polynomial time. We consider a graph H k (V , Ê ), named a k-replicated and-or series-parallel (RAOSP) graph, which is obtained from an AOSP graph H(V, E) by adding (k 0 1) replications to each vertex and adding proper edges between two vertices. It can be shown that the RAOSP graphs are not AOSP graphs; thus, the existing polynomial algorithm does not apply. Previously, only exponential time algorithms as used in general graphs are known for computing the reliability of H k (V , Ê ). In this paper, we present a linear time algorithm with O(K(ÉVÉ / ÉEÉ)) complexity to evaluate the reliability of the graph H k (V , Ê ), where K Å max{k 2 2 2 k , 2 3k }.

With the rapid development of wireless communication technology and the rapid increase in demand for network bandwidth, IEEE 802.16e is an emerging network technique that has been deployed in many metropolises. In addition to the features of high data rate and large coverage, it also enables scalable video multicasting, which is a potentially promising application, over an IEEE 802.16e network. How to optimally assign the modulation and coding scheme (MCS) of the scalable video stream for the mobile subscriber stations to improve spectral efficiency and maximize utility is a crucial task. We formulate this MCS assignment problem as an optimization problem, called the total utility maximization problem (TUMP). This article transforms the TUMP into a precedence constraint knapsack problem, which is a NP-complete problem. Then, a branch and bound method, which is based on two dominance rules and a lower bound, is presented to solve the TUMP. The simulation results show that the proposed branch and bound method can find the optimal solution efficiently.