TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing
This work addresses a data bottleneck for researchers and practitioners in Traditional Chinese Medicine by providing a benchmark to support AI-driven diagnosis, though it is incremental as it builds on existing NLP methods applied to a new domain-specific dataset.
The authors tackled the lack of high-quality datasets for syndrome differentiation in Traditional Chinese Medicine by introducing TCM-SD, a public large-scale dataset with 54,152 clinical records covering 148 syndromes, and proposed a domain-specific pre-trained language model, ZY-BERT, to establish performance baselines and reveal challenges in the task.
Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.