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.
Location information is critical to mobile wireless sensor networks (WSN) applications. With the help of location information, for example, routing can be performed more efficiently. In this paper, we propose a novel localization approach, Color-theory based Dynamic Localization (CDL), which is based on color theory to exploit localization in mobile WSNs. CDL makes use of the broadcast information, such as locations and RGB values, from all anchors (a small portion of nodes with GPS receivers attached), to help the server to create a location database and assist each sensor node to compute its RGB value. Then, the RGB values of all sensor nodes are sent to the server for localization of the sensor nodes. A unique feature of our color-theory based mechanism is that it can use one color to represent the distances of a sensor node to all anchors. Since CDL is easy to implement and is a centralized approach, it is very suitable for applications that need a centralized server to collect user (sensor) data and monitor user activities, such as community health-care systems and hospital monitoring systems. Evaluation results have shown that for mobile WSNs, the location accuracy of CDL (E-CDL, an enhanced version of CDL) is 40-50% (75-80%) better than that of MCL (Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of the 10th annual international conference on mobile computing and networking, pp. 45-57). In addition, we have implemented and validated our E-CDL algorithm on the MICAz Mote Developer's Kit.
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