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A Prediction Model of Clinical Diagnosis by The Combination of Traditional Chinese and Western Medicine Based on Data Mining

机译:基于数据挖掘的中西医结合临床诊断预测模型

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To improve the single type of the existing clinical diagnosis and treatment decision support system, this paper proposes an intelligent medical diagnosis model of integrated TCM and Western medicine based on data mining. The scheme mainly inputs the diagnosis data into two subsystems for training and testing, according to the characteristics of the clinical diagnosis support information environment. Firstly, the improved Apriori algorithm is used to mine the TCM diagnosis and treatment data to obtain more perfect diagnosis information; then the BP neural network optimized by GA is adopted to process the real samples to improve the medical diagnosis speed. Finally, a clinical diagnosis prototype system combining the two methods is designed by customs clearance development, which is applied in the specific decision support and achieves good mining effect, and the prediction data is basically consistent with the clinical practice. Therefore, the scheme has been proved to be feasible and helpful to reduce clinical medical errors.
机译:为了改进现有临床诊疗决策支持系统的单一类型,提出了一种基于数据挖掘的中西医结合的智能医疗诊断模型。该方案主要根据临床诊断支持信息环境的特点,将诊断数据输入两个子系统进行训练和测试。首先,利用改进的Apriori算法对中医诊疗数据进行挖掘,获得更完善的诊断信息;然后采用遗传算法优化的BP神经网络对实际样本进行处理,提高医学诊断速度。最后,通过海关通关开发,设计了结合这两种方法的临床诊断原型系统,并将其应用于具体的决策支持中,取得了良好的挖掘效果,预测数据与临床实践基本一致。因此,该方案被证明是可行的,有助于减少临床医疗差错。

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