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首页> 外文期刊>Journal of Computers >Analysis of Abnormality Diagnosis in Emergency Medicine by Integrating K-means and Decision Trees-a Case Study of Dongyang People’s Hospital in China
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Analysis of Abnormality Diagnosis in Emergency Medicine by Integrating K-means and Decision Trees-a Case Study of Dongyang People’s Hospital in China

机译：K-MEATION树木综合治疗急诊医学异常诊断 - 以中国东阳人民医院为例

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摘要

The performance of a triage system can facilitate patient classification in an emergency department, enabling patients in critical condition to receive better medical care; therefore, more perfect allocation and use of resources of emergency medical treatment are required. The correctness of nurses and doctors is related to triage medical care quality, patient satisfaction, and life safety. Hence, how to effectively extract experience by data mining and triage in the background of continuously increasing numbers of emergency patients is an issue worth exploring. Based on the case of Dongyang People’s Hospital in China, this study established a triage prediction model from process construction, parameter selection, and sampling, and randomly generated 501 samples of patients from the emergency database for cluster analysis (Ward’s method and K-means) and decision trees analysis upon data mining. The findings of this study show that the triage categorization of nurses is higher than that of doctors and most abnormal diagnoses occur to patients not examined on the date of admittance. The vital signs of pulse and temperature are more discerning. According to the confidence and support proportion, this study proposed seven association rules.
机译：分类系统的性能可以促进急诊部门的患者分类，使患者能够在危急情况下获得更好的医疗保健;因此，需要更完美的分配和使用紧急医疗资源。护士和医生的正确性与分类医疗保健品质，患者满意度和生命安全有关。因此，如何通过在急救患者持续增加的背景下有效地提取数据挖掘和分类的经验是值得探索的问题。基于中国东阳人民医院的案例，本研究建立了从过程建设，参数选择和采样的分类预测模型，并随机生成了来自紧急数据库的501个患者进行集群分析（Ward的方法和K-Means）和决策树木挖掘分析。本研究的结果表明，护士的分类分类高于医生的分类，大多数异常诊断都会发生在入场日期的患者。脉冲和温度的生命迹象更加敏锐。根据信心和支持比例，本研究提出了七条协会规则。

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