These findings support our hypothesis that sciatica pain is accompanied by the imbalance in inflammatory cytokines.

Travel time reliability has attracted increasing attention in recent years and is often listed as a major roadway performance and service quality measure for traffic engineers and travelers. Measuring travel time reliability is the first step toward improving it, ensuring on-time arrivals, and reducing travel costs. Most measures of travel time reliability derive from continuous probability distributions and apply to traffic data directly. However, little previous research shows a consensus for selection of a probability distribution family for travel time reliability. Different probability distribution families could yield different values for the same measure of travel time reliability (e.g., standard deviation). The authors believe that specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, they proposed two hypotheses for accurately measuring travel time reliability and designed an experiment to prove the two hypotheses. The first hypothesis was proved by ( a) conducting the Kolmogorov–Smirnov test and ( b) checking log likelihoods and the convergences of the corrected Akaike information criterion and of the Bayesian information criterion. The second hypothesis was proved by examining both moment- and percentile-based measures of travel time reliability. The results from testing the two hypotheses suggest that ( a) underfitting may cause disagreement in distribution selection, ( b) travel time can be precisely fitted by using mixture models with a higher value of K (regardless of distribution family), and ( c) measures of travel time reliability are insensitive to the selection of the distribution family. These findings allow researchers and practitioners to avoid testing of various distributions, and travel time reliability can be more accurately measured by using mixture models because of the higher values of log likelihoods.

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.

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