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Knowledge discovery in perioperative databases using bootstrapped rule induction.

机译:使用自举规则归纳法在围手术期数据库中发现知识。

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Analysis of medical databases can provide knowledge which may improve patient care. The goal of this research was to develop a Knowledge Discovery in Databases (KDD) approach for discovering relationships between perioperative variables, intraoperative events, and postoperative outcomes. A method for bootstrapping brute force rule induction was developed. The method reduces the number of rules an analyst must interpret, provides summary rules which indicate the rule's stability and variability, and uses multidimensional scaling to visualize rule similarity. A brute force rule induction algorithm is bootstrapped ten times. The variability in continuous attribute values, rule accuracy, and rule coverage is recorded for rules which persist across multiple replications. The method was applied to samples of increasing size to see the impact of sample size upon the stability of the rules generated.; The choices involved in conducting knowledge discovery in perioperative medicine are described. Rules were evaluated for clinical relevance. Experiments were conducted to determine if the inclusion of intraoperative and physiological data elements improved rule performance. Knowledge discovery was applied to the following postoperative outcomes: severe pain, the absence of pain, nausea and vomiting, and extended recovery time. Rules were also induced for the following intraoperative physiological incidents: hypertension, hypotension, low pulse pressure, low oxygen saturation, and high heart rate variability.; Where clinical guidelines are not established, practice patterns may reflect informal or unstated consensus among clinicians. Anesthetist practice patterns were induced from perioperative using bootstrapped rule induction to determine the drugs, anesthetic techniques, and postoperative orders associated with specific patient populations. Patterns were discovered for two populations: orthopedic patients undergoing arthroscopic surgery and obese women over fifty years old. The composition of practice patterns and the relationship between rule accuracy and coverage were explored. Coverage for twenty arthroscopic practice patterns exceeded ninety percent, suggesting informal consensus for managing arthroscopic procedures.
机译:医学数据库的分析可以提供可以改善患者护理的知识。这项研究的目的是开发一种数据库知识发现(KDD)方法,以发现围手术期变量,术中事件和术后结果之间的关系。开发了一种引导蛮力规则归纳的方法。该方法减少了分析人员必须解释的规则数量,提供了表明规则稳定性和可变性的摘要规则,并使用多维缩放来可视化规则相似性。蛮力规则归纳算法被引导十次。对于在多个复制中持续存在的规则,记录了连续属性值,规则准确性和规则覆盖范围的变化。该方法被应用于增加大小的样本,以查看样本大小对所生成规则的稳定性的影响。描述了在围手术期医学中进行知识发现所涉及的选择。对规则进行临床相关性评估。进行了实验,以确定是否包含术中和生理数据元素可以改善规则性能。知识发现适用于以下术后结果:严重疼痛,无疼痛,恶心和呕吐以及延长的恢复时间。还为以下术中生理事件制定了规则:高血压,低血压,低脉压,低氧饱和度和高心率变异性。如果没有建立临床指南,则实践模式可能反映临床医生之间非正式或未陈述的共识。围手术期采用引导法诱导麻醉师确定麻醉模式,以确定与特定患者人群相关的药物,麻醉技术和术后顺序。发现了两种人群的模式:接受关节镜手术的骨科患者和五十岁以上的肥胖女性。探索了练习模式的组成以及规则准确性和覆盖率之间的关系。二十种关节镜练习模式的覆盖率超过百分之九十,这表明在管理关节镜程序方面达成了非正式共识。

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