Efficient evidence selection for systematic reviews in traditional Chinese medicine
Abstract Purpose The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is often time-consuming and resource-intensive. In this study, we introduce a no...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02430-z |
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| author | Yizhen Li Zhe Huang Zhongzhi Luan Shujing Xu Yunan Zhang Lin Wu Darong Wu Dongran Han Yixing Liu |
| author_facet | Yizhen Li Zhe Huang Zhongzhi Luan Shujing Xu Yunan Zhang Lin Wu Darong Wu Dongran Han Yixing Liu |
| author_sort | Yizhen Li |
| collection | DOAJ |
| description | Abstract Purpose The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is often time-consuming and resource-intensive. In this study, we introduce a novel precision-preferred comprehensive information extraction and selection procedure to enhance both the efficiency and accuracy of evidence selection for TCM practitioners. Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. The selection process is recorded in real-time, allowing researchers to backtrack and verify its accuracy. This innovative approach was tested on ten high-quality, randomly selected systematic reviews of TCM-related topics written in Chinese. To evaluate its effectiveness, we compared the screening time and accuracy of this approach with traditional evidence selection methods. Results Our finding demonstrated that the new method accurately selected potential literature based on consistent criteria while significantly reducing the time required for the process. Additionally, in some cases, this approach identified a broader range of relevant evidence and enabled the tracking of selection progress for future reference. The study also revealed that traditional screening methods are often subjective and prone to errors, frequently resulting in the inclusion of literature that does not meet established standards. In contrast, our method offers a more accurate and efficient way to select clinical evidence for TCM practitioners, outperforming traditional manual approaches. Conclusion We proposed an innovative approach for selecting clinical evidence for TCM reviews and guidelines, aiming to reduce the workload for researchers. While this method showed promise in improving the efficiency and accuracy of evidence-based selection, its full potential required further validation. Additionally, it may serve as a useful tool for editors to assess manuscript quality in the future. |
| format | Article |
| id | doaj-art-be504199f81e48d4bb5cefda048195ad |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj-art-be504199f81e48d4bb5cefda048195ad2025-01-19T12:28:11ZengBMCBMC Medical Research Methodology1471-22882025-01-0125111110.1186/s12874-024-02430-zEfficient evidence selection for systematic reviews in traditional Chinese medicineYizhen Li0Zhe Huang1Zhongzhi Luan2Shujing Xu3Yunan Zhang4Lin Wu5Darong Wu6Dongran Han7Yixing Liu8Evidence-Based Medicine Center, The First Affiliated Hospital of Henan University of Chinese MedicineSchool of Life and Science, Beijing University of Chinese MedicineSchool of Computer Science and Engineering, Beihang UniversitySchool of Life and Science, Beijing University of Chinese MedicineSchool of Life and Science, Beijing University of Chinese MedicineSchool of Life and Science, Beijing University of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineSchool of Life and Science, Beijing University of Chinese MedicineSchool of Management, Beijing University of Chinese MedicineAbstract Purpose The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is often time-consuming and resource-intensive. In this study, we introduce a novel precision-preferred comprehensive information extraction and selection procedure to enhance both the efficiency and accuracy of evidence selection for TCM practitioners. Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. The selection process is recorded in real-time, allowing researchers to backtrack and verify its accuracy. This innovative approach was tested on ten high-quality, randomly selected systematic reviews of TCM-related topics written in Chinese. To evaluate its effectiveness, we compared the screening time and accuracy of this approach with traditional evidence selection methods. Results Our finding demonstrated that the new method accurately selected potential literature based on consistent criteria while significantly reducing the time required for the process. Additionally, in some cases, this approach identified a broader range of relevant evidence and enabled the tracking of selection progress for future reference. The study also revealed that traditional screening methods are often subjective and prone to errors, frequently resulting in the inclusion of literature that does not meet established standards. In contrast, our method offers a more accurate and efficient way to select clinical evidence for TCM practitioners, outperforming traditional manual approaches. Conclusion We proposed an innovative approach for selecting clinical evidence for TCM reviews and guidelines, aiming to reduce the workload for researchers. While this method showed promise in improving the efficiency and accuracy of evidence-based selection, its full potential required further validation. Additionally, it may serve as a useful tool for editors to assess manuscript quality in the future.https://doi.org/10.1186/s12874-024-02430-zTCM literatureSystematic reviewText miningEvidence-based medicine |
| spellingShingle | Yizhen Li Zhe Huang Zhongzhi Luan Shujing Xu Yunan Zhang Lin Wu Darong Wu Dongran Han Yixing Liu Efficient evidence selection for systematic reviews in traditional Chinese medicine BMC Medical Research Methodology TCM literature Systematic review Text mining Evidence-based medicine |
| title | Efficient evidence selection for systematic reviews in traditional Chinese medicine |
| title_full | Efficient evidence selection for systematic reviews in traditional Chinese medicine |
| title_fullStr | Efficient evidence selection for systematic reviews in traditional Chinese medicine |
| title_full_unstemmed | Efficient evidence selection for systematic reviews in traditional Chinese medicine |
| title_short | Efficient evidence selection for systematic reviews in traditional Chinese medicine |
| title_sort | efficient evidence selection for systematic reviews in traditional chinese medicine |
| topic | TCM literature Systematic review Text mining Evidence-based medicine |
| url | https://doi.org/10.1186/s12874-024-02430-z |
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