A systematic literature review and analysis of deep learning algorithms in mental disorders
Introduction: Mental disorders are the main cause of mortality and morbidity worldwide. Deep learning offers a considerable promise for mental health diagnosis and risk assessment. The current study considered the potential application of deep learning methods in mental disorders. Method: Four datab...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823001284 |
| _version_ | 1797785541386698752 |
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| author | Goli Arji Leila Erfannia Samira alirezaei Morteza Hemmat |
| author_facet | Goli Arji Leila Erfannia Samira alirezaei Morteza Hemmat |
| author_sort | Goli Arji |
| collection | DOAJ |
| description | Introduction: Mental disorders are the main cause of mortality and morbidity worldwide. Deep learning offers a considerable promise for mental health diagnosis and risk assessment. The current study considered the potential application of deep learning methods in mental disorders. Method: Four databases were reviewed between 2000 and February 2023, based on the PRISMA methodology. A total of 1339 papers was recognized and screened for their relevance to the use of deep learning algorithms in mental disease; 85 pertinent studies were identified and categorized based on several dimensions, such as subspecialty, deep learning methods, data sources, study limitations, and future directions. Result: The obtained result revealed that deep learning in mental health is vastly used for depression and mood recognition analysis. The Convolutional Neural Network (CNN) is a prominent method applied in selected studies. Conclusion: The results of this study may motivate further research on the use of deep learning in mental disorders and future directions for this promising technology. |
| first_indexed | 2024-03-13T00:55:31Z |
| format | Article |
| id | doaj.art-ca6b88365b64430c855f00c3a24a8da5 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-13T00:55:31Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-ca6b88365b64430c855f00c3a24a8da52023-07-07T04:27:23ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0140101284A systematic literature review and analysis of deep learning algorithms in mental disordersGoli Arji0Leila Erfannia1Samira alirezaei2Morteza Hemmat3Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Markazi, Iran; Corresponding author.Health Information Management Department, School of Health Management and Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, IranHealth Service Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Markazi, IranHealth Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Markazi, IranIntroduction: Mental disorders are the main cause of mortality and morbidity worldwide. Deep learning offers a considerable promise for mental health diagnosis and risk assessment. The current study considered the potential application of deep learning methods in mental disorders. Method: Four databases were reviewed between 2000 and February 2023, based on the PRISMA methodology. A total of 1339 papers was recognized and screened for their relevance to the use of deep learning algorithms in mental disease; 85 pertinent studies were identified and categorized based on several dimensions, such as subspecialty, deep learning methods, data sources, study limitations, and future directions. Result: The obtained result revealed that deep learning in mental health is vastly used for depression and mood recognition analysis. The Convolutional Neural Network (CNN) is a prominent method applied in selected studies. Conclusion: The results of this study may motivate further research on the use of deep learning in mental disorders and future directions for this promising technology.http://www.sciencedirect.com/science/article/pii/S2352914823001284Deep learningArtificial neural networksMental healthMental disordersLiterature review |
| spellingShingle | Goli Arji Leila Erfannia Samira alirezaei Morteza Hemmat A systematic literature review and analysis of deep learning algorithms in mental disorders Informatics in Medicine Unlocked Deep learning Artificial neural networks Mental health Mental disorders Literature review |
| title | A systematic literature review and analysis of deep learning algorithms in mental disorders |
| title_full | A systematic literature review and analysis of deep learning algorithms in mental disorders |
| title_fullStr | A systematic literature review and analysis of deep learning algorithms in mental disorders |
| title_full_unstemmed | A systematic literature review and analysis of deep learning algorithms in mental disorders |
| title_short | A systematic literature review and analysis of deep learning algorithms in mental disorders |
| title_sort | systematic literature review and analysis of deep learning algorithms in mental disorders |
| topic | Deep learning Artificial neural networks Mental health Mental disorders Literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823001284 |
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