Abstract
BackgroundSyndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients’ symptoms.MethodsTo alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS.ResultsExperimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome.ConclusionIn conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.
Highlights
Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM)
With the proposed simple yet effective attention-based MLP framewor (ATT-MLP), we evaluated our model on a real-world Acquired immune deficiency syndrome (AIDS) dataset that integrates data from multiple clinical units to provide a comprehensive view of syndrome differentiation and medication patterns of AIDS [22]
There are seven types of syndrome for these patients in the dataset according to the AIDS syndrome diagnostic criteria, which includes (S1) phlegm-heat obstructing the lung and accumulation of heat toxin; (S2) deficiency of both qi and yin in the lung and kidney; (S3) stasis of blood and toxin due to qi deficiency; (S4) hot in the liver with accumulation of damp toxin; (S5) stagnation of qi, phlegm and blood; (S6) deficiency of spleen and stomach with retention of damp; and (S7) qi deficiency with kidney yin deficiency
Summary
Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Syndrome diffetentiation in Traditional Chinese Medicine (TCM) syndromes is a method of classifying the whole functional status summarized by clinical symptoms of different individuals during a period of illness. In TCM, this is one of the crucial aspects to study syndromes and plays a guiding role in clinically individualized diagnosis and dialectical treatment of TCM. Differentiation is at the core of TCM and sets the precondition that ensures efficacy. The approaches for classifying the syndromes in TCM, which include multivariate statistical methods, machine learning, neural networks, and other methods introduced into the study, have resulted in an extensive set of scenarios.
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