IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity

doi:10.1609/aaai.v38i14.29491

	IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity
	Wang WJ(王雯婕)1 ; Pengfei Tang 3; Jian Lou 2; Yuanming Shao1 ; Lance Waller 3; Yi-an Ko 3; Li Xiong 3
	2024-03
会议录名称	2024 ASSOCIATION FOR THE ADVANCEMENT OF ARTIFICIAL INTELLIGENCE
ISSN	2159-5399
卷号	38
期号	14
页码	15634-15643
发表状态	已发表
DOI	10.1609/aaai.v38i14.29491
摘要	Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers' access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist: addressing the heterogeneity of features including temporal and non-temporal features, structurally missing values, and irregularity of the temporal measures, and ensuring rigorous privacy of the real data used for model training. Existing works in this domain only focused on solving one or two aforementioned challenges. In this work, we propose IGAMT, an innovative framework to generate privacy-preserved synthetic EHR data that not only maintains high quality with heterogeneous features, missing values, and irregular measures but also achieves differential privacy with enhanced privacy-utility trade-off. Extensive experiments prove that IGAMT significantly outperforms baseline and state-of-the-art models in terms of resemblance to real data and performance of downstream applications. Ablation studies also prove the effectiveness of the techniques applied in IGAMT. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
会议录编者/会议主办者	Association for the Advancement of Artificial Intelligence
关键词	Deep learning Economic and social effects eHealth Hospital data processing Learning systems Privacy-preserving techniques Records management Clinical research Downstream applications Electronic health Health records Machine-learning Missing values Outcome prediction Personalizations Privacy preserving Synthesization
会议名称	38th AAAI Conference on Artificial Intelligence, AAAI 2024
出版地	2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
会议地点	Vancouver, BC, Canada
会议日期	February 20, 2024 - February 27, 2024
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[62206207] ; National Institutes of Health (NIH)["R01LM013712","R01ES033241","UL1TR002378"] ; National Science Foundation (NSF)["IIS2302968","CNS-2124104","CNS-2125530"]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:001239983500051
出版者	Association for the Advancement of Artificial Intelligence
EI入藏号	20241515875784
EI主题词	Clinical research
EISSN	2374-3468
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 462.2 Hospitals, Equipment and Supplies ; 716 Telecommunication ; Radar, Radio and Television ; 718 Telephone Systems and Related Technologies ; Line Communications ; 723.2 Data Processing and Image Processing ; 971 Social Sciences
原始文献类型	Conference article (CA)
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/352512
专题	信息科学与技术学院信息科学与技术学院_本科生信息科学与技术学院_PI研究组_王雯婕组
通讯作者	Wang WJ(王雯婕); Jian Lou
作者单位	1.ShanghaiTech University 2.ZJU-Hangzhou Global Scientific and Technological Innovation Center 3.Emory University
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Wang WJ,Pengfei Tang,Jian Lou,et al. IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity[C]//Association for the Advancement of Artificial Intelligence. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:Association for the Advancement of Artificial Intelligence,2024:15634-15643.