Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/32146

Title: Clinical Trial Classification of SNS24 Calls with Neural Networks
Authors: Yang, Hua
Gonçalves, Teresa
Quaresma, Paulo
Vieira, Renata
Veladas, Rute
Pinto, Cátia
Oliveira, João
Ferreira, Maria
Morais, Jessica
Pereira, Ana
Fernandes, Nuno
Gonçalves, Carolina
Keywords: clinical triage
SNS24
deep learning
language models
clinical text classification;
Issue Date: Apr-2022
Publisher: MDPI
Citation: Yang, H.; Gonçalves, T.; Quaresma, P.; Vieira, R.; Veladas, R.; Pinto, C.S.; Oliveira, J.; Ferreira, M.C.; Morais, J.; Pereira, A.R.; Fernandes, N.; Gonçalves, C. Clinical Trial Classification of SNS24 Calls with Neural Networks. Future Internet 2022,14,130. https://doi.org/ 10.3390/fi14050130
Abstract: SNS24, the Portuguese National Health Contact Center, is a telephone and digital public ser- vice 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.
URI: https://www.mdpi.com/1999-5903/14/5/130/pdf
http://hdl.handle.net/10174/32146
Type: article
Appears in Collections:CIDEHUS - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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