[1]龚乐君,张立鹏,李宇茜,等.基于决策树的乳腺癌病历文本的挖掘与决策[J].南京师范大学学报(自然科学版),2019,42(03):42-51.[doi:10.3969/j.issn.1001-4616.2019.03.006]
　Gong Lejun,Zhang Lipeng,Li Yuxi,et al.Mining and Decision-Making of Breast Cancer MedicalRecord Text Based on Decision Tree[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):42-51.[doi:10.3969/j.issn.1001-4616.2019.03.006]
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基于决策树的乳腺癌病历文本的挖掘与决策()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
42-51
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Mining and Decision-Making of Breast Cancer MedicalRecord Text Based on Decision Tree
文章编号:
1001-4616(2019)03-0042-10
作者:
龚乐君1; 张立鹏1; 李宇茜1; 吴向辉1; 高志宏2; 潘传迪2; 杨 庚1
(1.江苏省大数据安全与智能处理重点实验室,南京邮电大学计算机学院、软件学院、网络空间安全学院,江苏 南京 210023)(2.浙江省智慧医疗工程技术研究中心,浙江 温州 325035)
Author(s):
Gong Lejun1; Zhang Lipeng1; Li Yuxi1; Wu Xianghui1; Gao Zhihong2; Pan Chuandi2; Yang Geng1
(1.Jiangsu Key Lab of Big Data Security & Intelligent Processing,School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)(2.Zhejiang Engineering Research Center of Intelligent Medicine,Wenzhou 325035,China)
关键词:
乳腺癌; 自然语言处理; 决策树; 文本挖掘; Neo4j
Keywords:
breast cancer; natural language processing; decision tree; text mining; Neo4j
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.006
文献标志码:
A
摘要:
乳腺癌是女性最常见的恶性肿瘤之一,严重威胁着世界范围内女性的健康,临床病历文本携带着经验丰富医生对疾病的诊断信息,对其挖掘,可获得乳腺癌相关的病况,从而可以辅助决策. 本文提交了一种方法从文本处理的角度,使用数据挖掘算法-决策树处理病历文本,挖掘乳腺癌疾病相关信息,对乳腺癌进行TNM及临床癌症分期决策,并对决策结果进行验证,同时结合Neo4j图数据库建立乳腺癌TNM-临床分期知识图谱,通过实例展示,该方法可得到乳腺癌的TNM与临床癌症分期病况. 表明提交的方法有望用来辅助医生进行决策.
Abstract:
Breast cancer is one of the most common malignant tumors in women,which seriously threatens the health of women worldwide. Clinical medical records carry the diagnostic information from experienced doctors. Mining these records could receive breast cancer-related conditions. This paper presents a method using data mining algorithm-Decision Tree to process medical records,to obtain breast cancer disease-related information via text processing. We conduct TNM and clinical cancer staging decisions for breast cancer and validate decision results. At the same time,we also combine the Neo4j-map database to establish breast cancer TNM-clinical staging knowledge map. The example shows that this method could obtain TNM and clinical cancer grading conditions for breast cancer. It indicates that the presented method is expected to be used to assist doctors in making decisions.

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备注/Memo

备注/Memo:
收稿日期:2019-07-16.基金项目:国家自然科学基金项目(61502243、61502247、61572263)、浙江省智慧医疗工程技术研究中心项目(2016E10011)、中国博士后基金(2018M632349)、江苏省高校自然科学基金(16KJB520003). 通讯联系人:龚乐君,博士,副教授,硕士生导师,研究方向:数据与文本挖掘,生物医学信息处理. E-mail:glj98226@163.com
更新日期/Last Update: 2019-09-30