# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 3 Materials and Methods
### Methods  
Our main task is to support the nurses with the clinical algorithm's choice. This can be framed as a supervised multi-class classification problem using as input the dataset we obtained from SNS24 calling records. The existing clinical dataset includes clinical texts and their corresponding clinical pathways are used for training a deep neural network model. Given a new record, the attribute \"clinical pathway\" is the class aiming to be predicted. As Figure 1 presents, the trained neural network model is used to predict the clinical pathway given the information collected by the nurses, and the predicted clinical pathway is finally returned to the nurse as the suggestion. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Id** & **Field (Portuguese)** & **Field (English)** \\ \hline
1 & ID encontro & meeting ID \\
2 & numero sns & sns number \\
3 & centro saude descricao & health center description \\
4 & data inicio & start date \\
5 & data disposica final & final disposition date \\
6 & data nascimento & birth date \\
7 & iadade & age \\
8 & genero & gender \\
9 & relacao com utente & relationship with user \\
10 & intencao inicial & initial intention \\
11 & motivo contacto & contact reason \\
12 & ultimo algoritmo & last algorithm \\
13 & disposica final & final disposition \\
14 & comentarios & comments \\
15 & unidade saude encaminhamento & referral health unit \\
16 & seguimento & follow-up \\
17 & tipologia interacao & interaction typology \\
18 & canal & channel \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The 18 fields recorded in the original dataset. The fields are listed in the original language Portuguese and English. Figure 1: Applying deep learning approaches on SNS24 clinical trial classification. The clinical texts which are recorded by SNS24 nurses are pre-processed and as the input of the neural network models. Using the designed deep learning architectures, neural network models are trained to predict clinical pathways and provide suggestions to the nurses. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Collecting Period** & **Records** & **Fields** & **Classes** \\ \hline Original & 1 January 2018–31 March 2018 & 269,658 & 18 fields & 52 \\ Experimental & 1 January 2018–31 March 2018 & 269,654 & “contact reason” field & 51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The statistics of the original and experimental dataset. Figure 2 presents the general framework for training a neural network model in our task. The design of a neural network model is highly depending on the selected architecture, such as the number of layers, the design of hidden layers, the pre-trained language model selection, etc. We propose to train neural network models using CNN, RNN, and transformers-based architectures:  
* CNN-based architecture. We use the architecture described by [PERSON] [18], which is a classical convolutional neural network for text classification. * RNN-based architecture. Mainly, two prevailing RNN types have been developed from the basic RNN [19]: long short-term memory (LSTM) [20] and gated recurrent unit (GRU) [21]. We use GRU type RNN in our experiments. * Transformers-based architecture. Transformers is a sequence-to-sequence architecture that was originally proposed for neural machine translation, and it shows effective performance in natural language processing tasks [22]. As Figure 2 shows, for each architecture, \"contact reason\" and \"clinical pathway\" are used as the articles and labels. They are pre-processed and fed to the neural network as the input. When embedding the input \"contact reason\" texts and building other hidden layers, pre-trained language models are used depending on the neural network architecture selection. Compared to the one-hot representation which maps words into high-dimensional and sparse data, the static distributed representation (word embeddings) maps the words into a low-dimensional continuous space and can capture the semantic meanings of words.