Efficient evidence selection for systematic reviews in traditional Chinese medicine.

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.