# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 3 Deep Learning Based MQA Approaches
### Autoencoders  
Autoencoders are artificial neural networks employed to learn effective data encoding in unsupervised problems, which aim to learn the representation (encoding) of a set of data by training the network to ignore noises in a signal. In addition to the reduction side, a reconstruction side is also learnt, in which the autoencoder tries to generate a representation from the reduced encoding that is more close to its original input. The main process of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approaches** & **References** & **Remarks** \\ \hline \multirow{4}{*}{Autoencoders based} & [PERSON], et al. (2019) [25] & \begin{tabular}{c} An inception convolution autoencoder model is proposed to address various issues, including high dimensionality, sparseness, noise, and non-professional expression. \\ \end{tabular} \\ \cline{2-3}  & [PERSON], et al. (2020) [59] & \begin{tabular}{c} The proposed deep ranking recursive autoencoders architecture is used for ranking question-candidate snippet answer pairs (Q-S) in order to get the most relevant candidate answers for biomedical questions. \\ \end{tabular} \\ \hline \multirow{4}{*}{CNN based} & [PERSON], et al. (2017) [60] & \begin{tabular}{c} A multiscale convolutional neural network framework is proposed to deal with Chinese medical question answer matching task at the character level. \\ \end{tabular} \\ \cline{2-3}  & [PERSON], et al. (2019) [61] & \begin{tabular}{c} A CNN-based semantic clustered representation (CSCR) is proposed to merge the word segments and bring forth a new representation that is compatible with deep matching models. \\ \end{tabular} \\ \hline \multirow{4}{*}{RNN based} & [PERSON], et al. (2017) [62] & \begin{tabular}{c} An LSTM model was proposed to classify relations from clinical notes. \\ They only used word embedding without manual feature engineering, but still achieved good results. \\ \end{tabular} \\ \cline{2-3}  & [PERSON], et al. (2019) [63] & \begin{tabular}{c} This entailment approach was proposed to identify entailment between two questions: premise and hypothesis. \\ \end{tabular} \\ \hline Recursive NN based & [PERSON], et al. (2014) [64] & \begin{tabular}{c} This recursive neural network based approach proved that, \\ paragraph-level representations results in good prediction comparing to individual sentence’s level representation. \\ \end{tabular} \\ \hline \multirow{4}{*}{Hybrid DNN based} & [PERSON], et al. (2017) [65] & \
\begin{tabular}{c} A CNN-LSTM attention model is proposed to predict user intents, and an unsupervised clustering method is applied to mine user intent taxonomy \\ \end{tabular} \\ \cline{2-3}  & [PERSON], et al. (2019) [66] &
\begin{tabular}{c} A hybrid model of CNN and GRU is employed to solve the problem of complex relationship between questions and answers, in order to enhance Chinese medical question answer selection. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: classification of papers based on approaches used. the autoencoder is shown in Figure 8. The final goal of the autoencoder is to combine a sequence of word vectors into a single vector, which has a fixed size and magnitude. At each step, it encodes two adjacent vectors, which satisfy a specific criterion as vectors. In the field of medical QASs, a convolutional autoencoder model for Chinese healthcare question clustering (ICAHC) was developed in [25], to solve the problem of sparsity, nonprofessional expression, high dimensionality, and noise. In this model, a set of kernels with different sizes was selected to explore both the diversity and quality in the clustering ensemble firstly, by using convolutional autoencoder networks. These kernels can capture diverse representations. Second, four ensemble operators are designed to merge representations based on whether they are independent. The output of these operators are input into the encoder. Finally, the features are mapped from the encoder into a lower-dimensional space. Recently, [PERSON], et al.