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

^*Corresponding author for this work

Matemáticas

Universidad Politécnica Salesiana, Quito
Universidad de las Américas - Ecuador

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

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%.

Original language	English
Title of host publication	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
Editors	Hasan Ayaz
Publisher	Springer Verlag
Pages	399-410
Number of pages	12
ISBN (Print)	9783030204723
DOIs	https://doi.org/10.1007/978-3-030-20473-0_39
State	Published - 2020
Event	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., United States Duration: 24 Jul 2019 → 28 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	953
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019
Country/Territory	United States
City	Washington D.C.
Period	24/07/19 → 28/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Diagnosis prediction
ICD-10
Machine learning
Mental disorders
TF-IDF
k-NN

Access to Document

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

Cite this

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. In 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. in 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., United States, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Psycho web

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%.

KW - Diagnosis prediction

KW - ICD-10

KW - Machine learning

KW - Mental disorders

KW - TF-IDF

KW - k-NN

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

M3 - Contribución a la conferencia

AN - SCOPUS:85067697989

SN - 9783030204723

T3 - Advances in Intelligent Systems and Computing

SP - 399

EP - 410

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

A2 - Ayaz, Hasan

PB - Springer Verlag

Y2 - 24 July 2019 through 28 July 2019

ER -

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. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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