AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records

Cui, Suhan; Wang, Jiaqi; Gui, Xinning; Wang, Ting; Ma, Fenglong

doi:10.1109/bibm55620.2022.9995209

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Book2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Predictive modeling using EHR data has attracted significant attention in recent years (Cui et al, 2022b;Xiao et al, 2018;. To enhance predictive performance, pretraining techniques have been explored.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Pretraining on Multimodal Electronic Health Records

Wang,

Luo,

Wang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MEDHMP 1 , specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MEDHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.

show abstract

Section: Related Workmentioning

confidence: 99%