Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method
arXiv:1801.09030
Abstract
Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas. In this paper, we propose a TCM prescription generation task that aims to automatically generate a herbal medicine prescription based on textual symptom descriptions. Sequence-to-sequence (seq2seq) model has been successful in dealing with sequence generation tasks. We explore a potential end-to-end solution to the TCM prescription generation task using seq2seq models. However, experiments show that directly applying seq2seq model leads to unfruitful results due to the repetition problem. To solve the problem, we propose a novel decoder with coverage mechanism and a novel soft loss function. The experimental results demonstrate the effectiveness of the proposed approach. Judged by professors who excel in TCM, the generated prescriptions are rated 7.3 out of 10. It shows that the model can indeed help with the prescribing procedure in real life.
References in corpus (7)
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Get To The Point: Summarization with Pointer-Generator Networks
- Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
- Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
- A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
- Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
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