AAAI 2023technical1 citations
Deep Learning for Medical Prediction in Electronic Health Records
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.
BibTeX
@article{Zhang_2024, title={Deep Learning for Medical Prediction in Electronic Health Records}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26933}, DOI={10.1609/aaai.v37i13.26933}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Xinlu}, year={2024}, month={Jul.}, pages={16145-16146} }