Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation
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.Obje...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549679/full |
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| author | YingShuai Wang YanLi Wan HongPu Hu HongPu Hu |
| author_facet | YingShuai Wang YanLi Wan HongPu Hu HongPu Hu |
| author_sort | YingShuai Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c94ba1aab1a148078a2d98bccef227e3 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-c94ba1aab1a148078a2d98bccef227e32025-08-20T03:34:18ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-08-011310.3389/fpubh.2025.15496791549679Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendationYingShuai Wang0YanLi Wan1HongPu Hu2HongPu Hu3Institute of Medical Information/Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaSchool of Marxism, School of Humanities and Social Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaBackgroundWith 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549679/fulldeep learningfeature engineeringrecommendation technologytraditional Chinese medicineclinical decision supportsmart healthcare |
| spellingShingle | YingShuai Wang YanLi Wan HongPu Hu HongPu Hu Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation Frontiers in Public Health deep learning feature engineering recommendation technology traditional Chinese medicine clinical decision support smart healthcare |
| title | Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation |
| title_full | Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation |
| title_fullStr | Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation |
| title_full_unstemmed | Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation |
| title_short | Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation |
| title_sort | multi task meta attention network for traditional chinese medicine diagnostic recommendation |
| topic | deep learning feature engineering recommendation technology traditional Chinese medicine clinical decision support smart healthcare |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549679/full |
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