# Clinical Trial Classification of SNS24 Calls with Neural Networks  
[PERSON]  
1 Department of Computer Science, University of Evora, 7000-671 Evora, Portugal; [EMAIL_ADDRESS] (P.Q. ); [EMAIL_ADDRESS] (R.V.) [PERSON]  
1 Department of Computer Science, University of Evora, 7000-671 Evora, Portugal; [EMAIL_ADDRESS] (P.Q. ); [EMAIL_ADDRESS] (R.V.) [PERSON]  
1 Department of Computer Science, University of Evora, 7000-671 Evora, Portugal; [EMAIL_ADDRESS] (P.Q. ); [EMAIL_ADDRESS] (R.V.) [PERSON]  
1 CIDETIUS, University of Evora, 7000-671 Evora, Portugal; [EMAIL_ADDRESS] (C.P. );  
[PERSON]  
1 CIDETIUS, University of Evora, 7000-800 Evora, Portugal; [EMAIL_ADDRESS] (C.P. );  
[PERSON]  
5 Servios Parthaloso do Ministerio da Sadade, 1050-091 Lisaba, Portugal;  
[EMAIL_ADDRESS] (C.S.P. ); [EMAIL_ADDRESS] (J.O. );  
[EMAIL_ADDRESS] (M.C.P. ); [EMAIL_ADDRESS] (J.M. );  
[PERSON].R.P. ); [EMAIL_ADDRESS] (N.F. );  
[PERSON]G.)  
[PERSON]  
5 Servios Parthaloso do Ministerio da Sadade, 1050-091 Lisaba, Portugal;  
[EMAIL_ADDRESS] (C.S.P. ); [EMAIL_ADDRESS] (J.O. );  
[EMAIL_ADDRESS] (M.C.P. ); [PERSON]] (J.M. );  
[EMAIL_ADDRESS] (C.G.P. );  
[EMAIL_ADDRESS] (C.G.P. ); [EMAIL_ADDRESS] (J.O. );  
[EMAIL_ADDRESS] (M.C.P. );  
[EMAIL_ADDRESS] (M.C.P. );  
[EMAIL_ADDRESS] (M.C.P. ); [EMAIL_ADDRESS] (J.  
pathways, the health professional or nurse selects the most appropriate one according to the citizen's self-reported symptoms and relevant information about medical history. The selection of the clinical algorithms leads to five possible final referrals: self-care, clinical assessment at a primary health care center, clinical assessment in hospital emergency, transference to the National Medical Emergency Institute (INEM), or transference to the Poison Information Center. All the 59 clinical pathways start by asking screening questions, aiming to identify critical situations which need immediate health care and that are rapidly transferred to National Medical Emergency Institute. For non-emergencies, nurses must ensure that the main symptoms reported by the citizen are correctly mapped to allow the choice of the clinical pathway. Age, gender, and clinical history should also be considered during this process since this information is often crucial to the selection of the most appropriate clinical pathway. The choice of the clinical pathway by the health care professionals in each telephone triage episode is extremely important since it will determine the final referral. These pathways use a risk-averse system of prioritization, as other triage protocols such as the Manchester Triage System [1]. For this reason, SNS24 has an important role in the identification of users' clinical situation and the correct referral to health services according to their symptoms. This work explores using neural network (NN) architectures to build classification models for SNS24 clinical triage, aiming to improve the quality and performance of telephone triage service; support the nurses in the triage process, particularly with clinical algorithm's choice; decrease the duration of the phone calls and allow a finer and faster interaction between citizens and SNS24 service. The main contributions of this paper are summarized as follows:  
* Three deep learning (DL) based classification architectures are compared, namely CNN (convolutional neural network), RNN (recurrent neural network), and transformers-based architecture, in the automatic clinical trial classification of the SNS24 health calls. * An empirical process for fine-tuning hyperparameters is explored. This process practically shortens the tuning time and theoretically achieves near-optimal classification accuracy. * Comprehensive experimental results show that neural network models outperform shallow machine learning models with the testing accuracies on the same SNS24 clinical dataset. * Useful suggestions are provided to the SNS24 health center. Practical analysis among similar clinical symptoms is carried out.