Clinical Trial Classification of SNS24 Calls with Neural Networks

Hua Yang, Teresa Gonçalves, Paulo Quaresma, Renata Vieira, Rute Veladas, Cátia Sousa Pinto, João Oliveira, Maria Cortes Ferreira, Jéssica Morais, Ana Raquel Pereira, Nuno Fernandes and Carolina Gonçalves
Additional contact information
Hua Yang: Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
Teresa Gonçalves: Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
Paulo Quaresma: Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
Renata Vieira: CIDEHUS, University of Évora, 7000-809 Évora, Portugal
Rute Veladas: Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
Cátia Sousa Pinto: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
João Oliveira: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
Maria Cortes Ferreira: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
Jéssica Morais: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
Ana Raquel Pereira: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
Nuno Fernandes: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal
Carolina Gonçalves: Serviços Partilhados do Ministério da Saúde, 1050-099 Lisboa, Portugal

Future Internet, 2022, vol. 14, issue 5, 1-26

Abstract: SNS24, the Portuguese National Health Contact Center, is a telephone and digital public service that provides clinical services. SNS24 plays an important role in the identification of users’ clinical situations according to their symptoms. Currently, there are a number of possible clinical algorithms defined, and selecting the appropriate clinical algorithm is very important in each telephone triage episode. Decreasing the duration of the phone calls and allowing a faster interaction between citizens and SNS24 service can further improve the performance of the telephone triage service. In this paper, we present a study using deep learning approaches to build classification models, aiming to support the nurses with the clinical algorithm’s choice. Three different deep learning architectures, namely convolutional neural network (CNN), recurrent neural network (RNN), and transformers-based approaches are applied across a total number of 269,654 call records belonging to 51 classes. The CNN, RNN, and transformers-based model each achieve an accuracy of 76.56%, 75.88%, and 78.15% over the test set in the preliminary experiments. Models using the transformers-based architecture are further fine-tuned, achieving an accuracy of 79.67% with Adam and 79.72% with SGD after learning rate fine-tuning; an accuracy of 79.96% with Adam and 79.76% with SGD after epochs fine-tuning; an accuracy of 80.57% with Adam after the batch size fine-tuning. Analysis of similar clinical symptoms is carried out using the fine-tuned neural network model. Comparisons are done over the labels predicted by the neural network model, the support vector machines model, and the original labels from SNS24. These results suggest that using deep learning is an effective and promising approach to aid the clinical triage of the SNS24 phone call services.

Keywords: deep learning; language models; clinical text classification; clinical triage; SNS24 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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