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首页> 外文期刊>BMC Medical Informatics and Decision Making >Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
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Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

机译：基于决策树的学习来预测患者控制的镇痛药的消耗和重新调整

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Background Appropriate 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. Methods The 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. Results The 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 Na?ve 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. Conclusion This 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.
机译：背景技术适当的术后疼痛管理有助于早期动员，缩短住院时间并降低成本。疼痛的治疗不足可能会阻碍短期康复，并对健康产生长期不利影响。这项研究的重点是患者自控镇痛（PCA），这是一种用于镇痛药物的输送系统。这项研究提出并演示了如何使用机器学习和数据挖掘技术来预测镇痛药需求和PCA调整。方法本研究的样本包括1099例患者。每个患者都有280个属性进行描述，包括class属性。除了通常研究的人口统计和生理因素外，本研究还强调与PCA相关的属性。我们基于PCA药物的最初几个小时，使用基于决策树的学习算法来预测镇痛药的消耗量和PCA控制的调整。我们还开发了一种基于最近邻居的数据清除方法，以减轻PCA设置重新调整预测中的类不平衡问题。结果决策树的总镇痛消耗（连续剂量和PCA剂量）和PCA镇痛需求（仅PCA剂量）的预测准确度分别为80.9％和73.1％。基于决策树的学习在镇痛剂消耗预测方面的表现优于人工神经网络，支持向量机，随机森林，旋转森林和朴素贝叶斯分类器。在PCA设置重新调整预测的研究中，提出的数据清理方法提高了每种学习方法的性能。比较分析从数据挖掘模型中确定了有益的属性，并将其与先前工作中报告的镇痛需要相关。结论本研究提出了麻醉中数据挖掘的实际应用。与以前的研究不同，本研究考虑了多种预测因素，包括随时间推移的PCA需求。我们分析了PCA患者的数据，并进行了一些实验，以评估应用机器学习算法协助麻醉师进行PCA管理的潜力。结果证明了所提出的整体方法在术后疼痛管理中的可行性。

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