28 Exploring intervention effects of digital twin city stress visualization systems on mental crisis early warning for occupational populations
Bibliographic record
Abstract
Introduction: Patients with schizophrenia face dual challenges of cognitive impairments and social deficits. AI-driven music interventions, as an emerging therapeutic tool, hold promise for improving outcomes, yet research on their specific impacts remains limited. This study investigates the effects of AI-based music therapies on cognitive function and social behavior in schizophrenia patients to develop targeted strategies for optimizing treatment efficacy and quality of life. Methods: The study was conducted using experimental methods, with 30 patients with schizophrenia selected as the research subjects. During the intervention process, AI technology is used to achieve personalized customization of music intervention. Specifically, AI systems use collaborative filtering algorithms to analyze patients’ past music listening records and identify their music preferences. Use YOLOv8 based facial recognition technology and neural network-based speech recognition technology to monitor the emotional state of patients. Thereby adjusting the music playlist in real-time, selecting the most suitable music type and rhythm for the patient’s current state. At the same time, the AI system also has built-in sensor devices to monitor patients’ reactions and dynamically optimize intervention plans based on feedback. At the beginning of the study and after the intervention, patients’ cognitive function and social behavior were evaluated using the Montreal Cognitive Assessment Scale (MoCA) and the Social Behavior Inventory (SBI), respectively. Results: This study has found that after AI driven music intervention, the cognitive function and social behavior of patients with schizophrenia have significantly improved. Before intervention, the average cognitive function score of patients was 58.2±7.5, and after intervention, it increased to 72.5±6.8 (P<0.001). The social behavior score increased from 42.0±8.5 points before the intervention to 58.0± 9.2 points after the intervention (P<0.001). This result indicates that patients have made positive progress in attention, memory, social interaction, and emotional expression, and the intervention effect is significant. Conclusion: Research has shown that AI driven music intervention, combined with facial expression recognition, speech recognition, and other technologies, achieves real-time monitoring and dynamic adjustment of patients’ emotional states, and has a significant positive impact on the cognitive function and social behavior of patients with schizophrenia. The application of these technologies provides an innovative and effective treatment approach for the field of mental health, and its long-term effects and applicability in different patient populations can be further explored in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".