Personalized Heart Disease Detection via ECG Digital Twin Generation
Abstract
One of the goals of neuro-symbolic artificial intelligence is to exploit background knowledge to improve the performance of learning tasks. However, most of the existing frameworks focus on the simplified scenario where knowledge does not change over time and does not cover the temporal dimension. In this work we consider the much more challenging problem of knowledge-driven sequence classification where different portions of knowledge must be employed at different timesteps, and temporal relations are available. Our extensive experimental evaluation compares multi-stage neuro-symbolic and neural-only architectures, and it is conducted on a newly-introduced benchmarking framework. Results not only demonstrate the challenging nature of this novel setting, but also highlight under-explored shortcomings of neuro-symbolic methods, representing a precious reference for future research.
Cite
Text
Hu et al. "Personalized Heart Disease Detection via ECG Digital Twin Generation." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/649Markdown
[Hu et al. "Personalized Heart Disease Detection via ECG Digital Twin Generation." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/hu2024ijcai-personalized/) doi:10.24963/ijcai.2024/649BibTeX
@inproceedings{hu2024ijcai-personalized,
title = {{Personalized Heart Disease Detection via ECG Digital Twin Generation}},
author = {Hu, Yaojun and Chen, Jintai and Hu, Lianting and Li, Dantong and Yan, Jiahuan and Ying, Haochao and Liang, Huiying and Wu, Jian},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {5872-5881},
doi = {10.24963/ijcai.2024/649},
url = {https://mlanthology.org/ijcai/2024/hu2024ijcai-personalized/}
}