# Reinforcement Learning-Based Intent Classification of Chinese Questions About Respiratory Diseases
## 1 Introduction  
With the relentless progression of information technology, online consultation has emerged as a pivotal medical service modality, indispensable in contemporary healthcare landscapes. The precise identification of patients' intentions not only enhances the efficiency of medical services but also fosters advancements in smart healthcare. Respiratory diseases, being the most prevalent category of illnesses, encompass a range of respiratory infectious diseases that exhibit characteristics such as diverse transmission routes and rapid transmission speeds. It is of paramount importance to promptly comprehend the latest advancements in respiratory infectious diseases. By analyzing the online intentions expressed by patients suffering from respiratory ailments, it becomes feasible to swiftly filter out consultations conducive to the dynamic assessment of infectious diseases. Consequently, the recognition of intentions among patients with respiratory diseases plays an important role in monitoring respiratory infectious diseases. The intent classification of Chinese questions about respiratory diseases (CQRD) is a type of text classification. It mainly focuses on the problems raised by patients on the Internet and judges their intent. Based on the analysis of CQRD samples and the recommendations of professional medical consultants, we divide them into five categories:* _How to treat_\((HT)\): The patient clearly states what kind of disease they are suffering from and asks about effective treatments (intervention measures). * _What disease_\((WD)\): The patient asks what disease they are suffering from or distinguishes the name of the specific disease from which they are suffering from. * _What will happen_\((WWH)\): The patient asks about the disease burden or adverse reactions that a certain disease or drug may cause. * _Why_\((WHY)\): The patient asks about the cause of a certain disease. * _Precautions_\((PRE)\): The main body of the patients are a specific group of people, asking about behaviors, diet, or other matters that require attention for a certain disease or drug. Compared to the general text dataset, the CQRD intent classification dataset exhibits the following distinctive features: online question platforms that lack standardized formats and presentation for questions and online medical inquiries that are characterized by their colloquial and flexible expression. Additionally, it encompasses a wide range of medical terminology (MT), primarily drug names (DrN), symptom names (SN), and disease names (DiN). Within the CQRD corpus, each record includes one or more intent-indicating words or phrases, termed keywords. The core of the intent classification of CQRD is text representation. With the advancement of neural networks, various text representation models have been proposed. The main components encompass a bag-of-words model [1], a sequence representation model [2], a structured model [3; 4], a graph neural network [5; 6], and attention-based methods [7]. In recent models, attention-based approaches have progressively gained prominence. A typical attention-based method is the BERT pre-training model [8] and it has achieved significant success in various natural language processing (NLP) tasks. Unlike BERT models trained on general corpora, MC-BERT [9] and BERT-ehealth [10] use Chinese medical and biological corpora for model training. In addition, contrastive learning [11; 12] can effectively mitigate the \"collapse\" issue of BERT and enhance the distinctiveness of sentence representation. Attention-based methods simulate human attention behavior by enabling models to focus on important parts of the input data while ignoring irrelevant information. Similarly, the impact of keywords on text representation is significant. The effective utilization of keywords not only enhances the quality of text representation but also addresses the interpretability limitations associated with attention-based methods. In the CQRD corpus, there exist numerous category-oriented words, which we refer to as keywords.