  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.
