Deep Learning for Medical Prediction in Electronic Health Records

Authors

  • Xinlu Zhang University of California, Santa Barbara

DOI:

https://doi.org/10.1609/aaai.v37i13.26933

Keywords:

Electronic Health Records (EHRs), Deep Learning, Clinical Notes, Time Series, Multimodal Learning

Abstract

The widespread adoption of electronic health records (EHRs) has opened up new opportunities for using deep neural networks to enhance healthcare. However, modeling EHR data can be challenging due to its complex properties, such as missing values, data scarcity in multi-hospital systems, and multimodal irregularity. How to tackle various issues in EHRs for improving medical prediction is challenging and under exploration. I separately illustrate my works to address these issues in EHRs and discuss potential future directions.
AAAI-23 Proceedings Cover

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Published

2023-09-06

How to Cite

Zhang, X. (2023). Deep Learning for Medical Prediction in Electronic Health Records. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16145-16146. https://doi.org/10.1609/aaai.v37i13.26933

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Doctoral Consortium Track