A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse
Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8678636/ |
| _version_ | 1819179390245797888 |
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| author | Hector Allende-Cid Juan Zamora Pedro Alfaro-Faccio Maria Francisca Alonso-Sanchez |
| author_facet | Hector Allende-Cid Juan Zamora Pedro Alfaro-Faccio Maria Francisca Alonso-Sanchez |
| author_sort | Hector Allende-Cid |
| collection | DOAJ |
| description | Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of natural language processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this paper, a novel dataset collected from a proper field study is presented. Also, grammatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by schizophrenic and non-schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with machine learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects. |
| first_indexed | 2024-12-22T21:57:36Z |
| format | Article |
| id | doaj.art-8a77d3d87cf24362a969b6fa1f350603 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:57:36Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8a77d3d87cf24362a969b6fa1f3506032022-12-21T18:11:12ZengIEEEIEEE Access2169-35362019-01-017455444555310.1109/ACCESS.2019.29086208678636A Machine Learning Approach for the Automatic Classification of Schizophrenic DiscourseHector Allende-Cid0Juan Zamora1https://orcid.org/0000-0003-0003-182XPedro Alfaro-Faccio2Maria Francisca Alonso-Sanchez3https://orcid.org/0000-0002-6638-1374Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileInstituto de Estadística, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileInstituto de Literatura y Ciencias del Lenguaje, Pontificia Universidad Católica de Valparaíso, Viña del Mar, ChileCentro de Investigación del Desarrollo en Cognición y Lenguaje, Universidad de Valparaíso, Viña del Mar, ChileSchizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of natural language processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this paper, a novel dataset collected from a proper field study is presented. Also, grammatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by schizophrenic and non-schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with machine learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects.https://ieeexplore.ieee.org/document/8678636/Applied machine learningnatural language processingschizophrenia |
| spellingShingle | Hector Allende-Cid Juan Zamora Pedro Alfaro-Faccio Maria Francisca Alonso-Sanchez A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse IEEE Access Applied machine learning natural language processing schizophrenia |
| title | A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse |
| title_full | A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse |
| title_fullStr | A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse |
| title_full_unstemmed | A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse |
| title_short | A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse |
| title_sort | machine learning approach for the automatic classification of schizophrenic discourse |
| topic | Applied machine learning natural language processing schizophrenia |
| url | https://ieeexplore.ieee.org/document/8678636/ |
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