Effects of Implementing a Tree Model of Diagnosis into a Bayesian Diagnostic Inference System

摘要：

　　To estimate a diagnostic probability similarly to experts using answers to interviews,we developed a system that fundamentally behaves as a Bayesian model.For predefined interviews,we defined the sensitivity and specificity related to one or more diagnoses.Additionally,we used a predefined parent-child relation between diagnoses to decrease the number of parameters to set.After calculating the disease probability,we trained the model using the difference of post-test probability between computer calculations and three experts opinions.We evaluated the effects of setting up tree structures.When using a tree structure,the model trained faster and produced better fitting results than the model without tree structure.Training with multiple raters training data confused the model.The scores worsened in later epochs.Herein,we present the new methods benefits and characteristics.

关键词： Machine Learning Decision Support Systems,Clinical Expert Systems

作者: Satoshi Iwai Yoshimasa Kawazoe Takeshi Imai Kazuhiko Ohe

作者单位: Department of Biomedical Informatics,Graduate School of Medicine,The University of Tokyo,Bunkyo-ku,Tokyo,Japan

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 882-886

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）