Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa
As mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to...
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/20/9447 |
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| author | Yuezhong Wu Huan Xie Lin Gu Rongrong Chen Shanshan Chen Fanglan Wang Yiwen Liu Lingjiao Chen Jinsong Tang |
| author_facet | Yuezhong Wu Huan Xie Lin Gu Rongrong Chen Shanshan Chen Fanglan Wang Yiwen Liu Lingjiao Chen Jinsong Tang |
| author_sort | Yuezhong Wu |
| collection | DOAJ |
| description | As mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to design an innovative intelligent auxiliary evaluation tool and a personalized medical-advice generation application, aiming to improve the efficiency of mental health assessments and the provision of personalized medical advice. The main contributions include the folowing: (1) Developing an auxiliary diagnostic tool that combines the Mini-International Neuropsychiatric Interview (M.I.N.I.) with finite state machines to systematically collect patient information for preliminary assessments; (2) Enhancing data processing by optimizing retrieval algorithms for efficient filtering and employing a fine-tuned RoBERTa model for deep semantic matching and analysis, ensuring accurate and personalized medical-advice generation; (3) Generating intelligent suggestions using NLP techniques; when semantic matching falls below a specific threshold, integrating medical-knowledge graphs to produce general medical advice. Experimental results show that this application achieves a semantic-matching degree of 0.9 and an accuracy of 0.87, significantly improving assessment accuracy and the ability to generate personalized medical advice. This optimizes the allocation of medical resources, enhances diagnostic efficiency, and provides a reference for advancing mental health care through artificial-intelligence technology. |
| format | Article |
| id | doaj-art-6ead08fabd134699998e46bdaf29e647 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6ead08fabd134699998e46bdaf29e6472025-08-20T02:11:04ZengMDPI AGApplied Sciences2076-34172024-10-011420944710.3390/app14209447Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTaYuezhong Wu0Huan Xie1Lin Gu2Rongrong Chen3Shanshan Chen4Fanglan Wang5Yiwen Liu6Lingjiao Chen7Jinsong Tang8School of Rail Transit, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Rail Transit, Hunan University of Technology, Zhuzhou 412007, ChinaRIKEN AIP (RIKEN Center for Advanced Intelligence Project (AIP)), Rigaku Kenkyujo Kakushin Chino Togo Kenkyu Senta, Tokyo 103-0027, JapanSchool of Business, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Medicine, Zhejiang University, Hangzhou 310020, ChinaSchool of Medicine, Zhejiang University, Hangzhou 310020, ChinaSchool of Computer Science, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Rail Transit, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Medicine, Zhejiang University, Hangzhou 310020, ChinaAs mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to design an innovative intelligent auxiliary evaluation tool and a personalized medical-advice generation application, aiming to improve the efficiency of mental health assessments and the provision of personalized medical advice. The main contributions include the folowing: (1) Developing an auxiliary diagnostic tool that combines the Mini-International Neuropsychiatric Interview (M.I.N.I.) with finite state machines to systematically collect patient information for preliminary assessments; (2) Enhancing data processing by optimizing retrieval algorithms for efficient filtering and employing a fine-tuned RoBERTa model for deep semantic matching and analysis, ensuring accurate and personalized medical-advice generation; (3) Generating intelligent suggestions using NLP techniques; when semantic matching falls below a specific threshold, integrating medical-knowledge graphs to produce general medical advice. Experimental results show that this application achieves a semantic-matching degree of 0.9 and an accuracy of 0.87, significantly improving assessment accuracy and the ability to generate personalized medical advice. This optimizes the allocation of medical resources, enhances diagnostic efficiency, and provides a reference for advancing mental health care through artificial-intelligence technology.https://www.mdpi.com/2076-3417/14/20/9447mental healthartificial intelligencenatural language processingmedical-knowledge graphautomatic generation |
| spellingShingle | Yuezhong Wu Huan Xie Lin Gu Rongrong Chen Shanshan Chen Fanglan Wang Yiwen Liu Lingjiao Chen Jinsong Tang Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa Applied Sciences mental health artificial intelligence natural language processing medical-knowledge graph automatic generation |
| title | Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa |
| title_full | Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa |
| title_fullStr | Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa |
| title_full_unstemmed | Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa |
| title_short | Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa |
| title_sort | advancing mental health care intelligent assessments and automated generation of personalized advice via m i n i and roberta |
| topic | mental health artificial intelligence natural language processing medical-knowledge graph automatic generation |
| url | https://www.mdpi.com/2076-3417/14/20/9447 |
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