Deep Learning for Medical Prediction in Electronic Health Records
DOI:
https://doi.org/10.1609/aaai.v37i13.26933Keywords:
Electronic Health Records (EHRs), Deep Learning, Clinical Notes, Time Series, Multimodal LearningAbstract
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.Downloads
Published
2024-07-15
How to Cite
Zhang, X. (2024). 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
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Section
AAAI Doctoral Consortium Track