面向病灶与其表征关联提取的核医学诊断文本挖掘

Mining Nuclear Medicine Diagnosis Text for Correlation Extraction Between Lesions and Their Representations

医学影像是现代临床医学疾病诊治不可或缺的重要组成部分,SPECT是功能影像的主要成像技术,广泛应用于肿瘤骨转移等疾病的诊治。SPECT诊断报告文本包含患者个人信息、图像描述和建议性结果等几个方面的信息。为准确提取SPECT核医学骨显像诊断文本中疾病与其表征之间的关联关系,研究并提出基于数据挖掘的核医学文本关联规则挖掘方法。首先,针对核医学诊断文本可能包含的信息冗余、数据缺失及表述不一致等问题,提出SPECT核医学诊断文本的预处理及统一编码方法;然后,应用经典的关联规则挖掘算法Apriori,提出病灶与表征之间关联的挖掘算法;最后,使用一组源自三甲医院核医学科的真实SPECT核医学诊断文本数据,验证了所提出的方法。结果表明,提出的方法客观提取了疾病与其表征之间的关联,获得的客观性评价指标平均值不低于90%。

Medical imaging is an indispensable part of the diagnosis and treatment of diseases in modern clinical medicine.SPECT is the main functional imaging technology and has been widely used in the diagnosis and treatment of diseases such as tumor bone metastasis.The SPECT diagnostic text contains several aspects of patients’personal information,image description,and suggested results.In order to accurately extract the association between disease and its representation in the diagnostic text of SPECT nuclear medicine bone imaging,a method of mining association rules of nuclear medicine text based on data mining is proposed.Firstly,a method of SPECT medical diagnostic text preprocessing and uniform coding is proposed to solve the problems of information redundancy,data loss and inconsistent expression.Secondly,the classical association rule mining algorithm Apriori is applied to propose the association mining algorithm between lesions and their representations.Finally,the proposed method is validated with a set of real-world SPECT nuclear medical diagnostic text data from the department of nuclear medicine in a 3a grade hospitals,and the results show that the proposed method is able to objectively extracted the association between the disease and its representation,and the average objectivity is more than 90%.