Neural Fine-Gray: Monotonic neural networks for competing risks

ArXiv Pub Date : 2023-05-11 DOI:10.48550/arXiv.2305.06703
V. Jeanselme, Changwon Yoon, Brian D. M. Tom, J. Barrett
{"title":"Neural Fine-Gray: Monotonic neural networks for competing risks","authors":"V. Jeanselme, Changwon Yoon, Brian D. M. Tom, J. Barrett","doi":"10.48550/arXiv.2305.06703","DOIUrl":null,"url":null,"abstract":"Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite competitive performances in tackling this problem, machine learning methods often ignore other competing risks that preclude the event of interest. This practice biases the survival estimation. Extensions to address this challenge often rely on parametric assumptions or numerical estimations leading to sub-optimal survival approximations. This paper leverages constrained monotonic neural networks to model each competing survival distribution. This modelling choice ensures the exact likelihood maximisation at a reduced computational cost by using automatic differentiation. The effectiveness of the solution is demonstrated on one synthetic and three medical datasets. Finally, we discuss the implications of considering competing risks when developing risk scores for medical practice.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2305.06703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite competitive performances in tackling this problem, machine learning methods often ignore other competing risks that preclude the event of interest. This practice biases the survival estimation. Extensions to address this challenge often rely on parametric assumptions or numerical estimations leading to sub-optimal survival approximations. This paper leverages constrained monotonic neural networks to model each competing survival distribution. This modelling choice ensures the exact likelihood maximisation at a reduced computational cost by using automatic differentiation. The effectiveness of the solution is demonstrated on one synthetic and three medical datasets. Finally, we discuss the implications of considering competing risks when developing risk scores for medical practice.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
神经细灰色:竞争风险的单调神经网络
时间到事件模型,即生存分析,不同于标准回归,因为它处理的是对没有经历感兴趣事件的患者的审查。尽管机器学习方法在解决这个问题方面表现出色,但它往往忽略了排除感兴趣事件的其他竞争风险。这种做法会使生存估计产生偏差。解决这一挑战的扩展通常依赖于参数假设或导致次优生存近似的数值估计。本文利用约束单调神经网络对各竞争生存分布进行建模。这种建模选择通过使用自动微分确保在减少计算成本的情况下实现精确的似然最大化。在一个合成数据集和三个医疗数据集上验证了该解决方案的有效性。最后,我们讨论的影响考虑竞争风险时,为医疗实践开发风险评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions. A Workflow to Create a High-Quality Protein-Ligand Binding Dataset for Training, Validation, and Prediction Tasks. Generating Novel Brain Morphology by Deforming Learned Templates. Node-reconfiguring multilayer networks of human brain function. Predicting Heteropolymer Phase Separation Using Two-Chain Contact Maps.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1