基于医疗知识库的辅助诊疗系统

Knowledge-based medical auxiliary diagnosis system

针对电子医疗信息日益增加而对其的组织利用率低的问题,提出了基于医疗知识库的辅助诊疗系统。首先,通过对医疗知识库中的疾病信息进行分词操作,建立倒排索引表;然后,计算输入的症状信息与相关疾病的相似度并进行排序,以作出诊断;其次,根据用户的反馈信息采用动态确定原有信息与反馈信息权重比的反馈查询方法进行优化诊断;最后,利用贝叶斯分类算法根据病例信息对进一步的检查方式作出推荐。实验结果表明,在所规定的统计策略下,初步诊断时系统的召回率达到95%,准确率达到85%,在优化诊断之后准确率达到95%。该系统以工具的形式给医疗工作者以提示,帮助提高医疗水平和质量。

In view of the problem that the electronic medical information is increasing and the utilization rate of the organization is low,the knowledge-based medical auxiliary diagnosis system was put forward.Firstly,the system established the inverted index table by using word segmentation of the disease information in medical knowledge base;secondly,the similarities between the input symptoms and the related diseases were calculated and sorted;then,the system used the weight ratio of the feedback information and the original was determined dynamically to optimize the diagnosis based on user feedback;finally,the Bayes classification algorithm was used to make recommendations based on illness cases.The experimental results show that under the statistical method specified here in the initial diagnosis system recall rate is 95% and accuracy rate is 85%,after optimizing diagnostic accuracy rate is 95%.The system in the form of tools gives tips for health care workers and helps to improve the level and quality of medical care.