Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data
<|reference_start|>Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data: The research explores the utilization of a deep learning model employing an attention mechanism in medical text mining. It targets the challenge of analyzing unstructured text information within medical data. This research seeks to enhance the model's capability to identify essential medical information by incorporating deep learning and attention mechanisms. This paper reviews the basic principles and typical model architecture of attention mechanisms and shows the effectiveness of their application in the tasks of disease prediction, drug side effect monitoring, and entity relationship extraction. Aiming at the particularity of medical texts, an adaptive attention model integrating domain knowledge is proposed, and its ability to understand medical terms and process complex contexts is optimized. The experiment verifies the model's effectiveness in improving task accuracy and robustness, especially when dealing with long text. The future research path of enhancing model interpretation, realizing cross-domain knowledge transfer, and adapting to low-resource scenarios is discussed in the research outlook, which provides a new perspective and method support for intelligent medical information processing and clinical decision assistance. Finally, cross-domain knowledge transfer and adaptation strategies for low-resource scenarios, providing theoretical basis and technical reference for promoting the development of intelligent medical information processing and clinical decision support systems.<|reference_end|>
@article{xiao2024exploration,
    title={Exploration of Attention Mechanism-Enhanced Deep Learning Models in the
  Mining of Medical Textual Data},
    author={Lingxi Xiao, Muqing Li, Yinqiu Feng, Meiqi Wang, Ziyi Zhu, Zexi Chen},
    journal={arXiv preprint arXiv:2406.00016},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00016},
    primaryClass={cs.CL}
}