Automated Emergency Room Triage: Helping Patients Get the Best Treatment

  • Alexandre Inoue Universidade de São Paulo
  • Marcus Prado Universidade de São Paulo
  • Fábio Cozman Universidade de São Paulo

Resumo


We describe an intelligent triage system for emergency rooms; the system interacts with patients and classifies them by priority level and with respect to medical specialty. The system consists of a conversational interface, coupled with sensors and a physical robot-like platform, and classifiers that operate on symptoms and measurements so as to select a medical specialty and to output a priority level. Tests with human subjects demonstrated that our Healthbot system was well received and in fact preferred to alternatives by most people. Tests have also shown that the classifiers reached accuracy consistent with a doctor's output.

Palavras-chave: Applications of Artificial Intelligence, Machine Learning, Fundamentals of Artificial Intelligence, Natural Language Processing, Data Science

Referências

Albuquerque, I. (2016) Joy Robot (Robô Da Alegria) - Open Source 3D Printed, Aduino Powered Robot! Available at: https://www.instructables.com/id/Joy-Robot-RoboDa-Alegria Open-Source-3D-Printed-A/.

T. R. M. Azeredo, H. M. Guedes, R. A. R. de Almeida, T. C. M. Chianca, J. C. A. Martins.Efficacy of the Manchester triage system: a systematic review. International Emergency Nursing, 23(2):47–52, 2015.

T. C. M. Chianca, R. M. Costa, M. V. Vidigal, L. C. R. Silva, G. A. Diniz, J. H. V. Araujo, C. C. Souza. Tempos de espera para atendimento usando Sistema de Triagem de Manchester em um hospital de urgência. REME – Revista Min. Enferm., volume 20, 2016

B. E. V. Comendador, B. M. B. Francisco, J. S. Medenilla, S. M. T. Nacion, T. B. E. Serac. Pharmabot: A pediatric generic medicine consultant chatbot. Journal of Automation and Control Engineering, 3(2):137–140, 2015.

A. Coucke, A. Saade, A. Ball, T. Bluche, A. Caulier, D, Leroy, C. Doumouro, T. Gisselbrecht, F, Caltagirone, T. Lavril, M. Primet, J, Dureau. Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces, https://arxiv.org/pdf/1805.10190.pdf, 2018.

V. B. E. R. Feijó, L. Cordoni Jr., R. K. T. de Souza, A. O. Dias. Análise da demanda atendida em unidade de urgência com classificação de risco. Saúde Debate 39(106):627– 636, 2015.

D. Jurafsky, J. H. Martin. Speech and Language Processing, 2018.

S. Levin, M. Toerper, E. Hamrock, J. S. Hinson, S. Barnes, H. Gardner, A. Dugas, B. Linton, T. Kirsch, G. Kelen. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index, Ann Emerg Med., 71(5):565–574, 2018.

K. Mackway-Jones, J. Marsden, J. Windle. Emergency Triage: Manchester Triage Group, 3rd Edition, 2014.

D. Milward, M. Beveridge. Ontology-based dialogue systems Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI03), 2003.

Ministério da Saúde do Brasil. Manual de Acolhimento e Classificação de Risco; 2018.

Y. Raita, T. Goto, M. K. Faridi, D. F. M. Brown, C. A. Camargo Jr, K. Hasegawa. Emergency department triage prediction of clinical outcomes using machine learning models, Crit. Care, 23(1):64, 2019.

X. Wang, A. Chused, N. Elhadad, C. Friedman, M. Markatou. Automated knowledge acquisition from clinical reports. AMIA Annu Symp Proc., pp. 783–787. 2008.

Albuquerque, I. (2016) Joy Robot (Robô Da Alegria) - Open Source 3D Printed, Aduino Powered Robot! Available at: https://www.instructables.com/id/Joy-Robot-Robo Da-Alegria-Open-Source-3D-Printed-A/.

T. R. M. Azeredo, H. M. Guedes, R. A. R. de Almeida, T. C. M. Chianca, J. C. A. Martins. Efficacy of the Manchester triage system: a systematic review. International Emergency Nursing, 23(2):47–52, 2015.

T. C. M. Chianca, R. M. Costa, M. V. Vidigal, L. C. R. Silva, G. A. Diniz, J. H. V. Araujo, C. C. Souza. Tempos de espera para atendimento usando Sistema de Triagem de Manchester em um hospital de urgência. REME – Revista Min. Enferm., volume 20, 2016

B. E. V. Comendador, B. M. B. Francisco, J. S. Medenilla, S. M. T. Nacion, T. B. E. Serac. Pharmabot: A pediatric generic medicine consultant chatbot. Journal of Automation and Control Engineering, 3(2):137–140, 2015.

A. Coucke, A. Saade, A. Ball, T. Bluche, A. Caulier, D, Leroy, C. Doumouro, T. Gisselbrecht, F, Caltagirone, T. Lavril, M. Primet, J, Dureau. Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces, https://arxiv.org/pdf/1805.10190.pdf, 2018.

V. B. E. R. Feijó, L. Cordoni Jr., R. K. T. de Souza, A. O. Dias. Análise da demanda atendida em unidade de urgência com classificação de risco. Saúde Debate 39(106):627– 636, 2015.

D. Jurafsky, J. H. Martin. Speech and Language Processing, 2018.

S. Levin, M. Toerper, E. Hamrock, J. S. Hinson, S. Barnes, H. Gardner, A. Dugas, B. Linton, T. Kirsch, G. Kelen. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index, Ann Emerg Med., 71(5):565–574, 2018.

K. Mackway-Jones, J. Marsden, J. Windle. Emergency Triage: Manchester Triage Group, 3rd Edition, 2014.

D. Milward, M. Beveridge. Ontology-based dialogue systems Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI03), 2003.

Ministério da Saúde do Brasil. Manual de Acolhimento e Classificação de Risco; 2018. Y. Raita, T. Goto, M. K. Faridi, D. F. M. Brown, C. A. Camargo Jr, K. Hasegawa. Emergency department triage prediction of clinical outcomes using machine learning models, Crit. Care, 23(1):64, 2019.

X. Wang, A. Chused, N. Elhadad, C. Friedman, M. Markatou. Automated knowledge acquisition from clinical reports. AMIA Annu Symp Proc., pp. 783–787. 2008.
Publicado
20/10/2020
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INOUE, Alexandre; PRADO, Marcus; COZMAN, Fábio. Automated Emergency Room Triage: Helping Patients Get the Best Treatment. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 591-602. DOI: https://doi.org/10.5753/eniac.2020.12162.