TCM Translator: A Sequence Generation Approach for Prescribing Herbal Medicines In traditional Chinese medicine (TCM), herbal prescriptions are accumulated from doctorsu0027 clinical experience and play an essential role in treatment process for more than thousands of years. Mining their containing manytomany relationship between symptoms and herbs are important for both clinical practice and novel prescriptions development. Previously, the research attention in this field is attracted by topic models and multilabel classification approaches, but they donu0027t fully capture the correlations of labels and achieve very promising performance. Alternatively, we problematic it as a machine translation problem, that the source sentence is formed by symptoms and the target sentence consists of herbs. Two sentences are both in weakly order. In terms of this assumption, we propose a novel sequencetosequence (seq2seq) architecture, namely TCM translator, to translate symptoms to herbs. Seq2seq, consisting of an encoder and a decoder, is a well known framework for resolving the machine translation problem in natural language processing (NLP) community. It maps a bag of symptoms to the latent space, which is decoded to the target sentence, i.e., herbs. In this paper, transformer is used as our encoder to explore the latent correlations of symptoms and LSTM is implemented as the decoder to generate the herbs sequentially. The experimental results demonstrate that our proposed approach is effective and outperforms other baseline models by a substantial margin.

Semisupervised learning for classification on Chinese drug treatment questions Construction of automatic Question Answering (QA) system for online healthcare community gains great attention from the researchers due to the lack of practitioners who can respond precise answers to patientsu0027 consultations. In this paper, we focus on drug treatment question classification task, which can help building QA system for such questions. Due to the lack of labeled data and the high cost of labeling work, we consider using documents or texts from the internet which are related to our task. We fetch a large amount of unlabeled questionanswer pairs and drug instructions from the internet and design a cotraining style method (which is a semisupervised learning method) to utilize them. Using some specified models as classifiers, we prove that the classifier trained under our method outperforms the same model trained under supervised learning with less labeled data as input.