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Clinical Trial Classification of SNS24 Calls with Neural Networks

Author

Listed:
  • Hua Yang

    (Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
    Department of Computer Science, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Teresa Gonçalves

    (Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
    Centro ALGORITMI, Vista Lab, University of Évora, 7000-671 Évora, Portugal)

  • Paulo Quaresma

    (Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
    Centro ALGORITMI, Vista Lab, 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)

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.

Suggested Citation

  • 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 & Carolin, 2022. "Clinical Trial Classification of SNS24 Calls with Neural Networks," Future Internet, MDPI, vol. 14(5), pages 1-26, April.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:130-:d:802971
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    Cited by:

    1. Teresa Gonçalves & Rute Veladas & Hua Yang & Renata Vieira & Paulo Quaresma & Paulo Infante & Cátia Sousa Pinto & João Oliveira & Maria Cortes Ferreira & Jéssica Morais & Ana Raquel Pereira & Nuno Fer, 2023. "Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation," Future Internet, MDPI, vol. 15(1), pages 1-25, January.

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