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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10883 |
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| _version_ | 1866912204734857216 |
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| author | Yang, Hongyi Chang, Fangyuan Zhu, Dian Fumie, Muroi Liu, Zhao |
| author_facet | Yang, Hongyi Chang, Fangyuan Zhu, Dian Fumie, Muroi Liu, Zhao |
| contents | This systematic review assessed the current state and future prospects of artificial intelligence (AI) in schizophrenia rehabilitation management. We reviewed 61 studies on AI-related data types, feature engineering methods, algorithmic models, and evaluation metrics published from 2012-2024. The review categorizes AI applications into the following key application areas: symptom monitoring, medication management, risk management, functional training, and psychosocial support. Findings indicate that supervised machine learning techniques, particularly for symptom monitoring and relapse risk management, remain the predominant approaches, effectively leveraging structured data while incorporating interpretable algorithms. This study underscores the potential of AI in transforming long-term management strategies for schizophrenia, offering valuable insights into improving the quality of life of patients. Future research should focus on expanding data sources through multimodal data integration, exploring deep learning models, and integrating AI-driven interventions into training tasks to fully capitalize on AI's potential in schizophrenia rehabilitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review Yang, Hongyi Chang, Fangyuan Zhu, Dian Fumie, Muroi Liu, Zhao Artificial Intelligence This systematic review assessed the current state and future prospects of artificial intelligence (AI) in schizophrenia rehabilitation management. We reviewed 61 studies on AI-related data types, feature engineering methods, algorithmic models, and evaluation metrics published from 2012-2024. The review categorizes AI applications into the following key application areas: symptom monitoring, medication management, risk management, functional training, and psychosocial support. Findings indicate that supervised machine learning techniques, particularly for symptom monitoring and relapse risk management, remain the predominant approaches, effectively leveraging structured data while incorporating interpretable algorithms. This study underscores the potential of AI in transforming long-term management strategies for schizophrenia, offering valuable insights into improving the quality of life of patients. Future research should focus on expanding data sources through multimodal data integration, exploring deep learning models, and integrating AI-driven interventions into training tasks to fully capitalize on AI's potential in schizophrenia rehabilitation. |
| title | Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.10883 |