# A Novel Neural Network-Based Method for Medical Text Classification
## 1 Introduction  
As a classic task of natural language processing, text classification can quickly find the corresponding categories from massive data and realize automatic classification [1]. Therefore, text classification plays an essential role in text data retrieval and mining. However, text classification in a specific area can lead to high dimensionality and data sparsity problems, especially in the medical field. Medical text contains medical records and medical literature [2]. The former is a record of the medical activity process of the doctor's examination, diagnosis and treatment and development of the patient's disease. It describes the patient's medical history and the effect of the prescription. It is the detailed information of the patient during treatment. The latter is a documentary record of the research results of the latest medical methods. Both are important clinical information resources. With the development of information technology and the popularity of electronic medical records, huge amount of electronic medical record texts and medical literature have been accumulated, providing valuable data resources for information mining in the medical field. Medical text generally contains normalized medical terminology, which refers to some concept or abbreviations in the medical field, such as blood pressure of 140/65. Besides, medical records often have poor grammatical sentences [3; 4]. Therefore, text classification in the medical domain is more challenging. Since the deep learning method achieves good performance in image classification and speech recognition, it has been widely used in the field of natural language processing in recent years andachieves good results. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are the most commonly used deep learning methods and play an important role in text categorization. At the same time, current classification studies on medical texts are directed to the classification of specific medical texts, such as classifications specific to electronic medical records or classifications for medical literature. [PERSON] et al. develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBert) [5]. It uncovers high-quality relationships between medical concepts as judged by humans but it is the specific study in clinical notes. It is difficult to find a universal classification model that performs well in both medical records and medical literature. It is difficult to find a universal classification model that performs well in both medical records and medical literature. Based on this, we propose a novel and unified hierarchical neural network method for medical text. The method constructs sentence representations of sentences by segmenting the document and then aggregating those into the document representation. In the sentence representation, the convolutional layer extracts features from the sentence and bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. In order to verify the performance of the proposed approach, four comprehensive labeled datasets of experiments (including two medical record datasets and two medical literature datasets) are conducted. Compared with other state-of-the-art text classification methods, the experimental results clearly show that our method is effective. The main contributions of the paper are as follows:  
1. In order to solve the problem of high-dimensionality of medical texts, we propose a new hierarchical neural network method. 2.