Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa

• Published in: Applied sciences 2024-10, Vol.14 (20), p.9447
• Main Authors: Wu, Yuezhong, Xie, Huan, Gu, Lin, Chen, Rongrong, Chen, Shanshan, Wang, Fanglan, Liu, Yiwen, Chen, Lingjiao, Tang, Jinsong
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; automatic generation; Automation; Computational linguistics; Customization; Datasets; Diagnostic equipment (Medical); Efficiency; Health aspects; Health services; Information retrieval; Innovations; Knowledge representation; Language processing; Medical practices; medical-knowledge graph; Mental disorders; mental health; Mental health care; Natural language interfaces; natural language processing; Patient education; Patients; Psychiatric services; Questionnaires; Research methodology; Semantics
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14209447

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.