Mental health prediction using machine learning: taxonomy,applications, and challenges
The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. )is paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Fur...
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| Format: | Article |
| Language: | en |
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Hindawi
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/44521/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/44521/ https://doi.org/10.1155/2022/9970363 |
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| _version_ | 1838232251905605632 |
|---|---|
| author | Jetli Chung Jason Teo |
| author_facet | Jetli Chung Jason Teo |
| author_sort | Jetli Chung |
| building | UMS Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sabah |
| content_source | UMS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. )is paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. )en, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field. |
| format | Article |
| id | my.ums.eprints-44521 |
| institution | Universiti Malaysia Sabah |
| language | en |
| publishDate | 2022 |
| publisher | Hindawi |
| record_format | eprints |
| spelling | my.ums.eprints-445212025-07-16T09:17:13Z https://eprints.ums.edu.my/id/eprint/44521/ Mental health prediction using machine learning: taxonomy,applications, and challenges Jetli Chung Jason Teo QR75-99.5 Bacteria RC475-489 Therapeutics. Psychotherapy The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. )is paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. )en, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field. Hindawi 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/44521/1/FULL%20TEXT.pdf Jetli Chung and Jason Teo (2022) Mental health prediction using machine learning: taxonomy,applications, and challenges. Applied Computational Intelligence and So Computing, 2022. pp. 1-19. https://doi.org/10.1155/2022/9970363 |
| spellingShingle | QR75-99.5 Bacteria RC475-489 Therapeutics. Psychotherapy Jetli Chung Jason Teo Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title | Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title_full | Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title_fullStr | Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title_full_unstemmed | Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title_short | Mental health prediction using machine learning: taxonomy,applications, and challenges |
| title_sort | mental health prediction using machine learning: taxonomy,applications, and challenges |
| topic | QR75-99.5 Bacteria RC475-489 Therapeutics. Psychotherapy |
| url | https://eprints.ums.edu.my/id/eprint/44521/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/44521/ https://doi.org/10.1155/2022/9970363 |
| url_provider | http://eprints.ums.edu.my/ |