# An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM
## 2 Related Work
### Medical Question Classification
[28] created a new corpus for consumer cancer-related questions and built an Expected Answer Type (EAT) taxonomy to automatically classify these questions. This taxonomy was developed based on supervised machine-learning-based methods, and achieved an F1-score (see Section 4.2) of 96.3%. They took into consideration the dimensionality reduction and spelling correct, but the results showed that their method has a small impact on question classifier results. They concluded that statistical classification methods address effectively the problem of natural imbalance in the types of questions asked by users, particularly differentiating factual and patient-specific questions. [PERSON] et al. [29] examined the intentions of many Chinese health questions on the Internet. They demonstrated the intention recognition problem as a text classification problem. Two techniques were used to improve the learning-based text classifier by location-based and area-based feature weightings, which can achieve a Macro-averaged F1-score of 73.1% and Micro-averaged F1-score of 82.9%. Here, the location-based technique means that a word in a health question is intention-indicative if it appears at the beginning or the end of the health question, while the area-based technique means that a health question is likely to have an intention if it has many words that appear in the area of the health question. Other good works are as follows: [PERSON] et al. [30] presented a model, where they focused on automating the classification of doctor-patient questions by simulating the consultations with virtual patients. They classified questions by looking up data in the clinical record using the computational strategy, and achieved an Average F1-score of 81.2%. [PERSON] et al. [31] developed a manually annotated dataset of medication question-answer pairs based on real consumer questions submitted to MedlinePlus. They also proposed recurrent and convolutional neural networks for question-type identification and focus recognition. The results showed that they achieved an accuracy of 75.7%. [PERSON] et al. [32] demonstrated that the ladder approach can give the highest performance, which incorporates the knowledge representation of the hierarchical evidence taxonomy. The results showed that they achieved an accuracy of 57%. However, the classification performance categorized into five categories is not high due to the shortage of training data. Moreover, the problem of ambiguity in Population, Intervention, Comparator, and Outcome (PICO) sentence prediction tasks are studied in [33]. They discussed the impact that annotations for training named entity recognition systems have on training a high-performing and flexible architecture for question answering. They also mentioned that the augmentation approach can solve the problem of insufficient amounts of training annotations for PICO entity extraction, where the augmentation approach is a technique that generates additional training data by applying various transformations or modifications to the existing datasets. [PERSON] et al. [34] presented a rule-based approach for QC in the healthcare domain. Their approach used 500 medical questions collected from patients and doctors, to classify questions into various categories based on the two-layered taxonomy of 6-course grain and 50 fine-grained categories developed by [PERSON] and [PERSON]. Nevertheless, the accuracy of the \"how\" type of questions is very low (44%). The reason for this is that the classifier fails to identify the matching pattern of the question, and ends up assigning it to the wrong category. As introduced above, different word and sentence embedding models have been proposed, and these models have a significant role in categorizing medical questions during the classification process. By taking advantage of the limitations of the existing works, we proposed a CNN-BiLSTM model to address some of these problems for a better classifier of medical questions.