[1]张大鹏,程学亮,孙明霞.DeephitTM:医学生存分析的时间相关性深度学习模型[J].南京师大学报(自然科学版),2024,(03):138-148.[doi:10.3969/j.issn.1001-4616.2024.03.017]
 Zhang Dapeng,Cheng Xueliang,Sun Mingxia.DeephitTM:a Time-dependent Deep Learning Model for Medical Survival Analysis[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(03):138-148.[doi:10.3969/j.issn.1001-4616.2024.03.017]
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DeephitTM:医学生存分析的时间相关性深度学习模型()
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《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

卷:
期数:
2024年03期
页码:
138-148
栏目:
计算机科学与技术
出版日期:
2024-09-15

文章信息/Info

Title:
DeephitTM:a Time-dependent Deep Learning Model for Medical Survival Analysis
文章编号:
1001-4616(2024)03-0138-11
作者:
张大鹏12程学亮2孙明霞1
(1.江苏信息职业技术学院物联网工程学院,江苏 无锡 214153)
(2.燕山大学信息科学与工程学院,河北 秦皇岛 066004)
Author(s):
Zhang Dapeng12Cheng Xueliang2Sun Mingxia1
(1.School of IoT Engineering,Jiangsu Vocational College of Information Technology,Wuxi 214153,China)
(2.College of information Science and Engineering,Yanshan University,Qinhuangdao 066004,China)
关键词:
生存分析深度学习时间相关性神经网络Deephit模型
Keywords:
survival analysisdeep learningtemporal correlationneural networkDeephit mode
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2024.03.017
文献标志码:
A
摘要:
生存分析是医学中经常用到的一种健康预测方法,越来越多的学者开始采用深度学习的方法对生存分析问题进行建模以得到更好的预测结果. 目前已有的方法都假设风险和时间的联合概率是无关联的. 然而生存分析数据的实际结果中却包含时间因素,这就无法保证不同时刻得到的风险概率是无关联的. 本文提出一种带有时间相关性的深度学习模型DeephitTM,该模型对已有的深度学习模型Deephit进行了改进. 实验结果表明,在不同的数据集上,改进后的模型的性能相比于原模型能够提升1到3个百分点.
Abstract:
Survival analysis is a health prediction method often used in medicine. More and more scholars start to use deep learning method to model survival analysis problems to get better prediction results. Currently,existing methods assume that the joint probability of risk and time is uncorrelated,but the actual results of survival analysis data contain time factors,which cannot guarantee that the risk probability obtained at different times is uncorrelated. This paper proposes a time-dependent deep learning model,DeephitTM,to improve the existing deep learning model Deephit. Experimental results show that the performance of our model can be improved by 1 to 3 percentage points compared with the original model on different data sets.

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备注/Memo

备注/Memo:
收稿日期:2022-09-20.
基金项目:国家自然科学基金项目(61973261)、江苏省高等学校自然科学研究面上项目(18KJD510011)、江苏省高等职业教育高水平专业群建设项目(苏教职函〔2021〕1号).
通讯作者:张大鹏,博士,副教授,研究方向:机器学习. E-mail:daniao@ysu.edu.cn
更新日期/Last Update: 2024-09-15