Psycho web: A machine learning platform for the diagnosis and classification of mental disorders

Paulina Morillo; Holger Ortega; Diana Chauca; Julio Proaño; Diego Vallejo-Huanga; María Cazares

doi:10.1007/978-3-030-20473-0_39

Psycho web: A machine learning platform for the diagnosis and classification of mental disorders

Paulina Morillo, Holger Ortega, Diana Chauca, Julio Proaño, Diego Vallejo-Huanga, María Cazares

Matemáticas

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

8 Citas (Scopus)

Resumen

In this paper, we present the development of a platform to collect data from cases diagnosed with mental disorders. It includes the use of a Machine Learning classification algorithm, k-NN with TF-IDF, to automatically identify the type of mental disorder suffered by a patient given his/her symptoms, when evaluated by a mental health professional. The platform called “Psycho Web” has a friendly web interface that will allow ergonomic interaction between the mental health professional and the system. The dataset used for the initial evaluation of our platform is composed of 114 instances in total, 56% of which were obtained from the taxonomy proposed by ICD-10. The rest of the instances correspond to real cases, whose symptoms and diagnoses were taken by professionals who voluntarily collaborated with the project. A raw application of the algorithm to the data available shows results with errors that go down to 5%.

Idioma original	Inglés
Título de la publicación alojada	Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019
Editores	Hasan Ayaz
Editorial	Springer Verlag
Páginas	399-410
Número de páginas	12
ISBN (versión impresa)	9783030204723
DOI	https://doi.org/10.1007/978-3-030-20473-0_39
Estado	Publicada - 2020
Evento	AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019 - Washington D.C., Estados Unidos Duración: 24 jul. 2019 → 28 jul. 2019

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	953
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019
País/Territorio	Estados Unidos
Ciudad	Washington D.C.
Período	24/07/19 → 28/07/19

ODS de las Naciones Unidas

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10.1007/978-3-030-20473-0_39

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Morillo, P., Ortega, H., Chauca, D., Proaño, J., Vallejo-Huanga, D., & Cazares, M. (2020). Psycho web: A machine learning platform for the diagnosis and classification of mental disorders. En H. Ayaz (Ed.), Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019 (pp. 399-410). (Advances in Intelligent Systems and Computing; Vol. 953). Springer Verlag. https://doi.org/10.1007/978-3-030-20473-0_39

Morillo, Paulina ; Ortega, Holger ; Chauca, Diana et al. / Psycho web : A machine learning platform for the diagnosis and classification of mental disorders. Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019. editor / Hasan Ayaz. Springer Verlag, 2020. pp. 399-410 (Advances in Intelligent Systems and Computing).

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abstract = "In this paper, we present the development of a platform to collect data from cases diagnosed with mental disorders. It includes the use of a Machine Learning classification algorithm, k-NN with TF-IDF, to automatically identify the type of mental disorder suffered by a patient given his/her symptoms, when evaluated by a mental health professional. The platform called “Psycho Web” has a friendly web interface that will allow ergonomic interaction between the mental health professional and the system. The dataset used for the initial evaluation of our platform is composed of 114 instances in total, 56% of which were obtained from the taxonomy proposed by ICD-10. The rest of the instances correspond to real cases, whose symptoms and diagnoses were taken by professionals who voluntarily collaborated with the project. A raw application of the algorithm to the data available shows results with errors that go down to 5%.",

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Morillo, P, Ortega, H, Chauca, D, Proaño, J, Vallejo-Huanga, D & Cazares, M 2020, Psycho web: A machine learning platform for the diagnosis and classification of mental disorders. En H Ayaz (ed.), Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019. Advances in Intelligent Systems and Computing, vol. 953, Springer Verlag, pp. 399-410, AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019, Washington D.C., Estados Unidos, 24/07/19. https://doi.org/10.1007/978-3-030-20473-0_39

Psycho web: A machine learning platform for the diagnosis and classification of mental disorders. / Morillo, Paulina; Ortega, Holger; Chauca, Diana et al.
Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019. ed. / Hasan Ayaz. Springer Verlag, 2020. p. 399-410 (Advances in Intelligent Systems and Computing; Vol. 953).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T2 - AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019

AU - Morillo, Paulina

AU - Ortega, Holger

AU - Chauca, Diana

AU - Proaño, Julio

AU - Vallejo-Huanga, Diego

AU - Cazares, María

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - In this paper, we present the development of a platform to collect data from cases diagnosed with mental disorders. It includes the use of a Machine Learning classification algorithm, k-NN with TF-IDF, to automatically identify the type of mental disorder suffered by a patient given his/her symptoms, when evaluated by a mental health professional. The platform called “Psycho Web” has a friendly web interface that will allow ergonomic interaction between the mental health professional and the system. The dataset used for the initial evaluation of our platform is composed of 114 instances in total, 56% of which were obtained from the taxonomy proposed by ICD-10. The rest of the instances correspond to real cases, whose symptoms and diagnoses were taken by professionals who voluntarily collaborated with the project. A raw application of the algorithm to the data available shows results with errors that go down to 5%.

AB - In this paper, we present the development of a platform to collect data from cases diagnosed with mental disorders. It includes the use of a Machine Learning classification algorithm, k-NN with TF-IDF, to automatically identify the type of mental disorder suffered by a patient given his/her symptoms, when evaluated by a mental health professional. The platform called “Psycho Web” has a friendly web interface that will allow ergonomic interaction between the mental health professional and the system. The dataset used for the initial evaluation of our platform is composed of 114 instances in total, 56% of which were obtained from the taxonomy proposed by ICD-10. The rest of the instances correspond to real cases, whose symptoms and diagnoses were taken by professionals who voluntarily collaborated with the project. A raw application of the algorithm to the data available shows results with errors that go down to 5%.

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SN - 9783030204723

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BT - Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019

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Morillo P, Ortega H, Chauca D, Proaño J, Vallejo-Huanga D, Cazares M. Psycho web: A machine learning platform for the diagnosis and classification of mental disorders. En Ayaz H, editor, Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019. Springer Verlag. 2020. p. 399-410. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-20473-0_39

Psycho web: A machine learning platform for the diagnosis and classification of mental disorders

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