基于节点输入策略贝叶斯网络的骨盆骨折分型研究
Pelvic Fracture Classificaiton Based on the Bayesian Network of Node Ordering Strategy
投稿时间:2016-11-10  修订日期:2017-05-11
DOI:10.11908/j.issn.0253-374x.2017.08.019     稿件编号:    中图分类号:TP181
 
摘要点击次数: 799    全文下载次数: 92
中文摘要
      基于历史数据的统计和收集,选取骨盆骨折患者存在的18个体表特征,采用基于K2算法的贝叶斯网络方法挖掘各体表特征之间和骨盆骨折类型与体表特征间的相互关系;设计不同的节点输入策略,分析不同输入策略对算法性能的影响;基于骨盆稳定性将骨盆骨折分成A、B、C三种类型,分别找到与其直接相关的体表特征,作为判断骨盆骨折类型的依据.基于体表特征和骨盆骨折类型的分析结果,借助早期的观察及简单检查,对患者进行初步分型.
英文摘要
      Based on the statistics and collection of historical data, 18 surface characteristics of patients with pelvic fractures were selected. Bayesian network based on K2 algorithm was used to mine the causal relationship between the 18 surface characteristics, also between the surface characteristics and the pelvic fracture types. Different node ordering strategies were designed to analyze the influence on algorithm performance. Based on the stability of the pelvis, pelvic fracture was divided into A, B and C 3 types. Then found the features associated with each type of pelvic fracture, which was the basis of judgment. Based on the analysis of surface characteristics and pelvic fracture types, preliminary classification were made by means of early observation and simple examination.
HTML   查看全文  查看/发表评论  

您是第3633273位访问者
版权所有《同济大学学报(自然科学版)》
主管单位:教育部 主办单位:同济大学
地  址: 上海市四平路1239号 邮编:200092 电话:021-65982344 E-mail: zrxb@tongji.edu.cn
本系统由北京勤云科技发展有限公司设计