1 research outputs found
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
Herb recommendation plays a crucial role in the therapeutic process of
Traditional Chinese Medicine(TCM), which aims to recommend a set of herbs to
treat the symptoms of a patient. While several machine learning methods have
been developed for herb recommendation, they are limited in modeling only the
interactions between herbs and symptoms, and ignoring the intermediate process
of syndrome induction. When performing TCM diagnostics, an experienced doctor
typically induces syndromes from the patient's symptoms and then suggests herbs
based on the induced syndromes. As such, we believe the induction of syndromes,
an overall description of the symptoms, is important for herb recommendation
and should be properly handled. However, due to the ambiguity and complexity of
syndrome induction, most prescriptions lack the explicit ground truth of
syndromes. In this paper, we propose a new method that takes the implicit
syndrome induction process into account for herb recommendation. Given a set of
symptoms to treat, we aim to generate an overall syndrome representation by
effectively fusing the embeddings of all the symptoms in the set, to mimic how
a doctor induces the syndromes. Towards symptom embedding learning, we
additionally construct a symptom-symptom graph from the input prescriptions for
capturing the relations between symptoms; we then build graph convolution
networks(GCNs) on both symptom-symptom and symptom-herb graphs to learn symptom
embedding. Similarly, we construct a herb-herb graph and build GCNs on both
herb-herb and symptom-herb graphs to learn herb embedding, which is finally
interacted with the syndrome representation to predict the scores of herbs. In
this way, more comprehensive representations can be obtained. We conduct
extensive experiments on a public TCM dataset, showing significant improvements
over state-of-the-art herb recommendation methods.Comment: Accepted by ICDE 202