[1]周 琦,万亚平,左建宏,等.基于因果推断肺癌患者预后治疗影响因素研究[J].计算机技术与发展,2021,31(08):145-149.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 025]
　ZHOU Qi,WAN Ya-ping,ZUO Jian-hong,et al.Research on Influencing Factors of Prognosis Treatment for Patients withLung Cancer Based on Causal Inference[J].,2021,31(08):145-149.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 025]
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基于因果推断肺癌患者预后治疗影响因素研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年08期
页码:
145-149
栏目:
应用前沿与综合
出版日期:
2021-08-10

文章信息/Info

Title:
Research on Influencing Factors of Prognosis Treatment for Patients withLung Cancer Based on Causal Inference
文章编号:
1673-629X(2021)08-0145-05
作者:
周 琦1; 万亚平1; 2; 左建宏3; 刘 纯1; 马真真1; 杨菁华1
1. 南华大学 计算机学院,湖南 衡阳 421001;
2. 湖南省医疗大数据国际科技合作基地,湖南 衡阳 421001;
3. 南华大学附属第三医院,湖南 衡阳 421001
Author(s):
ZHOU Qi1; WAN Ya-ping1; 2; ZUO Jian-hong3; LIU Chun1; MA Zhen-zhen1; YANG Jing-hua1
1. School of Computers,University of South China,Hengyang 421001,China;
2. Hunan Medical Big Data International Technology Cooperation Base,Hengyang 421001,China;
3. The Third Affiliated Hospital of South China University,Hengyang 421001,China
关键词:
因果推断; 线性非高斯模型; 统计; 肺癌; 预后因素
Keywords:
causal inference; LiNGAM; statistics; lung cancer; prognosis treatment
分类号:
TP81
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 08. 025
摘要:
因果推断作为强人工智能的基础,越来越受到科研人员的关注并开始将其应用到各行各业。 肺癌是世界范围内癌症发病率和死亡率居高不下的主要原因,患者的生存率低预后差以至于癌症的复发率高。 因此确定肿瘤患者预后的因素变得尤为重要。 针对传统医学统计方法只能从仅有的观测数据中学习表观现象的建模关联,而没有深入挖掘其隐藏的因果方向,无法回答某些干预和反事实问题,提出基于线性非高斯模型(LiNGAM)的肺癌患者临床数据因果发现方法,可以得出临床病理特征之间的因果路线图。 实验结果表明,血小板可以作为肺癌患者预后评估的一个检测指标,从因果推断的角度出发,可以准确判断患者预后,为临床治疗提供有效的干预。 为因果推断的应用领域提供了新的研究方向。
Abstract:
As the basis of strong artificial intelligence, causal inference has been paid more and more attention by researchers and has been applied to all walks of life. Lung? cancer is the main cause of high cancer incidence and mortality worldwide. The low survival rate and poor prognosis of patients lead to the high recurrence rate of cancer. Therefore, it is quite important to determine the prognostic factors of tumor patients. In view of the fact that traditional medical statistical methods can only? ? ? learn the modeling association of the apparent phenomena from the only observed data,and can’ t answer some intervention and counter factual, we propose a causal discovery method of lung cancer patients’ clinical data based on linear non Gaussian model (lingam),which can obtain the causal roadmap between clinical pathological features. The experiment shows that platelets can be used as a detection index for prognosis evaluation of lung cancer patients. From the perspective of causal inference,it can accurately judge the prognosis of patients,and provide effective intervention for clinical treatment. It provides a new research direction for the application of causal inference.

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更新日期/Last Update: 2021-08-10