AUTHOR=Wang YingShuai , Wan YanLi , Hu HongPu 

TITLE=Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1549679

DOI=10.3389/fpubh.2025.1549679

ISSN=2296-2565

ABSTRACT=BackgroundWith the continuous growth of medical data and advancements in medical technology, there is an increasing need for personalized and accurate assisted diagnosis. However, implementing recommendation systems in healthcare presents numerous challenges, requiring further in-depth research.ObjectiveThis study explores the application of recommendation technology in smart healthcare. The primary goal is to design a deep learning model that effectively integrates medical knowledge for improved diagnostic support.MethodsWe first developed a feature engineering process tailored to the characteristics and requirements of medical data. This process involved data preparation, feature selection and transformation to extract informative features. Subsequently, a knowledge-matching deep learning model was designed to analyze and predict medical data. This model enhances evaluation metrics through its capabilities in nonlinear fitting and feature learning.ResultsExperimental results indicate that our proposed deep learning model achieves an average improvement of +2.7% in the core metrics Hits@10 compared to baseline models in the Traditional Chinese Medicine (TCM) clinical recommendation scenario. The model effectively processes medical data, providing accurate predictions and valuable insights to support clinical decision-making.ConclusionThis research provides significant support for the advancement and application of smart medical technology. Our deep learning model demonstrates strong potential for medical data analysis and clinical decision-making. It can contribute to enhanced healthcare quality and efficiency, ultimately advancing the medical field.