Deep Survival Analysis With Latent Clustering and Contrastive Learning

doi:10.1109/JBHI.2024.3362850

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	Deep Survival Analysis With Latent Clustering and Contrastive Learning
	Cui, Chang 1,2; Tang, Yongqiang 2; Zhang, Wensheng 1,2
刊名	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
	2024-05-01
卷号	28 期号:5 页码:3090-3101
关键词	Analytical models Task analysis Self-supervised learning Predictive models Hazards Data models Correlation Survival analysis deep learning clustering contrastive learning survival prediction
ISSN号	2168-2194
DOI	10.1109/JBHI.2024.3362850
通讯作者	Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要	Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance. In this study, we propose a novel Deep Survival Analysis model with latent Clustering and Contrastive learning (DSACC). Specifically, we jointly optimize representation learning, latent clustering and survival prediction in a unified framework. In this way, the clusters distribution structure in latent representation space is revealed, and meanwhile the structure of the clusters is well incorporated to improve the ability of survival prediction. Besides, by virtue of the learned clusters, we further propose a contrastive loss function, where the uncensored data in each cluster are set as anchors, and the censored data are treated as positive/negative sample pairs according to whether they belong to the same cluster or not. This design enables the censored data to make full use of the supervision information of the uncensored samples. Through extensive experiments on four popular clinical datasets, we demonstrate that our proposed DSACC achieves advanced performance in terms of both C-index (0.6722, 0.6793, 0.6350, and 0.7943) and Integrated Brier Score (IBS) (0.1616, 0.1826, 0.2028, and 0.1120).
资助项目	National Key Research and Development Program of China
WOS关键词	CENSORED-DATA ; MODEL ; REGRESSION ; SUBPOPULATIONS ; PREDICTION ; PROGNOSIS
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001221547700015
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58444]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Tang, Yongqiang; Zhang, Wensheng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cui, Chang,Tang, Yongqiang,Zhang, Wensheng. Deep Survival Analysis With Latent Clustering and Contrastive Learning[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,28(5):3090-3101.
APA	Cui, Chang,Tang, Yongqiang,&Zhang, Wensheng.(2024).Deep Survival Analysis With Latent Clustering and Contrastive Learning.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(5),3090-3101.
MLA	Cui, Chang,et al."Deep Survival Analysis With Latent Clustering and Contrastive Learning".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.5(2024):3090-3101.