Background and objective: Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side indicate unhealthiness, which may result in diseases. Yin-yang disharmony is considered to be the cause of pathological changes. Syndrome differentiation of yin-yang is crucial to clinical diagnosis. It lays a foundation for subsequent medical judgments, including therapeutic methods, and formula, among many others. However, because of the complexities of the mechanisms and manifestations of disease, it is difficult to exactly point out which one, yin or yang, is disharmonious. There has been inadequate research conducted on syndrome differentiation of yin and yang from a computational perspective. In this study, we present a computational method, viz. an end-to-end syndrome differentiation of yin deficiency and yang deficiency.
Methods: Unlike most previous studies on syndrome differentiation, which use structured datasets, this study takes unstructured texts in medical records as its inputs. It models syndrome differentiation as a task of text classification. This study experiments on two state-of-the-art end-to-end algorithms for text classification, i.e. a classic convolutional neural network (CNN) and fastText. These two systems take the n-grams of several types of tokens as their inputs, including characters, terms, and words.
Results: When evaluated on a data set with 7326 modern medical records in TCM, it is observed that CNN and fastText generally give rise to comparable performances. The best accuracy rate of 92.55% comes from the system taking inputs as raw as n-grams of characters. It implies that one can build at least a moderate system for the differentiation of yin deficiency and yang deficiency even if he has no glossary or tokenizer at hand.
Conclusions: This study has demonstrated the feasibility of using end-to-end text classification algorithms to differentiate yin deficiency and yang deficiency on unstructured medical records.
Keywords: Convolutional neural networks; End-to-end; FastText; Syndrome differentiation; Text classification; Traditional Chinese medicine; Yang deficiency; Yin deficiency.
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