Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models-Reference-Cited by-同舟云学术

Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models

Published:2024-08-24 Issue: Volume: Page:4607-4618
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Zhong Yuan¹^ORCID,Wang Xiaochen¹^ORCID,Wang Jiaqi¹^ORCID,Zhang Xiaokun²^ORCID,Wang Yaqing³^ORCID,Huai Mengdi⁴^ORCID,Xiao Cao⁵^ORCID,Ma Fenglong⁶^ORCID

Affiliation:

1. The Pennsylvania State University, University Park, PA, USA

2. Dalian University of Technology, Dalian, Liaoning, China

3. Purdue University, West Lafayette, IN, USA

4. Iowa State University, Ames, IA, USA

5. GE Healthcare, Seattle, WA, USA

6. The Penn State University, University Park, PA, USA

Funder

National Institutes of Health

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671836

Reference38 articles.

1. Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. 2021. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems (2021), 17981--17993.

2. Efficient Learning Machines

3. Mrinal Kanti Baowaly, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. 2019. Synthesizing electronic health records using improved generative adversarial networks. JAMIA (2019), 228--241.

4. Siddharth Biswal, Soumya Ghosh, Jon Duke, Bradley Malin, Walter Stewart, Cao Xiao, and Jimeng Sun. 2021. EVA: Generating longitudinal electronic health records using conditional variational autoencoders. In Machine Learning for Healthcare Conference. 260--282.

5. George EP Box and David A Pierce. 1970. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Amer. Statist. Assoc. (1970) 1509--1526.