# A Novel Neural Network-Based Method for Medical Text Classification
## 4 Experiments
### Results  
#### 4.3.1 Overall Comparison  
In this section, our evaluation results are shown on the medical texts (medical records and medical literature) classification task. Some approach analysis are given.  
The comparison results for medical texts ( TCM, CCKS, AIM, Hallmarks) are presented in Table 2. The best results are shown in boldface. From Table 2, among the seven approaches mentioned above, our approach outperforms other baselines on four datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Datasets** & **Classes** & **Sentence Length** & **Dataset Size** & **Vocab Size** & **Training Set** & **Validation Set** & **Test Set** \\ \hline TCM & 5 & 428 & 5413 & 99,243 & 3789 & 1082 & 541 \\ CCKS & 4 & 73 & 400 & 2980 & 280 & 80 & 40 \\ Hallmarks & 3 & 833 & 8474 & 29,141 & 5931 & 1694 & 847 \\ AIM & 2 & 833 & 2648 & 29,141 & 1853 & 529 & 264 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset statistics. The results are based on the test set.  
In medical record datasets, the results of our method are 89.09%, 93.75% for TCM and CCKS datasets. Our method gives the relative improvements of 12.47%, 4.38% compared to CNN on TCM dataset and CCK dataset, respectively. RCNN, HAN, AC-BiLSTM are all well-classed methods in general text classification recently. However, our method far exceeds these methods in the medical records data. This is because the professional vocabulary in the medical record is more dense than the general news text. The general text classification algorithm which performs well in the news domain does not produce a good classification effect on the medical record. However, our method makes use of the attention mechanism in the word level and sentence level to extract important vocabulary, which solves the problem of data sparsity. Therefore, we can find that our model is effective for medical record texts.  
In medical literature classification, our method outperforms two traditional machine learning methods (SVM, Logistic Regression) and other deep learning methods (CNN, LSTM, RCNN, HAN, AC-BiLSTM) on two datasets (AIM and Hallmarks). This is because the professional vocabulary of the medical literature is much more sparse than the medical record and our method is better for text categorization with professional vocabulary, which has higher density. Therefore, the classification method that generally works well in the general field can achieve good results in the classification of medical literature but our method is still effective compared with the latest and classic methods. For the dataset Hallmarks, Fasttext is the only method to arrive at above 80% but our method has the closest result to fasttext. It demonstrates that with our method, as an end to end model, the results are still promising and comparable with those models.  
Combined with the results in medical record classification and medical literature classification, our results consistently outperform the most of the published baseline models. In view of the above discussion it can be concluded that the overall performance of our method is effective for the medical texts, especially for medical records.