14 ANALYSIS OF THE EFFECTIVENESS OF DIGITAL EMOTION RECOGNITION DISPLAY DESIGN IN EARLY INTERVENTION FOR SCHIZOPHRENIA
Authors: Liu Jia , Zhou* Xianjie , Sun Yue 
Published: February 2025

Background

Schizophrenia is a chronic and severe mental disorder characterized by disorders of thinking, hallucinations, delusions, and emotional and social dysfunction. Due to their unique psychological development stage and social environment, college students are of special importance for the early identification and intervention of schizophrenia. Early diagnosis and treatment of schizophrenia is essential to improve prognosis, reduce disability, and improve quality of life. However, due to the complexity and heterogeneity of schizophrenia, early diagnosis and treatment pathway selection face many challenges. Knowledge management, as a systematic approach, is able to integrate and optimize information, knowledge, and skills to provide support for the early diagnosis and treatment of schizophrenia. Study aims to explore how to through the perspective of knowledge management, combined with clinical symptoms, drug response, cognitive health and mental related factors, optimize the early diagnosis of schizophrenia and treatment path, and explore the special group of schizophrenia patients’ health care utilization and cost benefit, in order to provide more accurate and more effective mental health services for college students.

Methods

Using the case-control study method, 300 college students who met the diagnostic criteria for schizophrenia and 150 healthy college students were selected as the control group. By using neuroimaging techniques such as Functional Magnetic Resonance Imaging (fMRI) and Wisconsin Card Sorting Test (WCST), the cognitive and social functions of two groups of students were evaluated. Meanwhile, the visual scanning path pattern analysis technique was used to examine the patient’s ability to process facial emotion perception. After data collection, multiple linear regression analysis was used to explore the relationship between cognitive function, social function, and facial emotion perception and the severity of schizophrenia symptoms.

Results

The results showed that the schizophrenia patient group performed significantly less in cognitive function tests and social function assessment than the control group (P&lt;0.01). Multiple linear regression analysis showed that cognitive function (β=0.65, P&lt;0.001) and social function (β=0.52, P&lt;0.001) were significant predictors of symptom severity in schizophrenia. In addition, there was a significant correlation between facial emotion perceptual processing ability and symptom severity (β=0.48, P&lt;0.01). In terms of treatment pathway optimization, patient treatment adherence and quality of life can be significantly improved through knowledge management strategies such as patient education, family support and integration of community resources (P&lt;0.05).

Discussion

The results reveal the key role of cognitive function, social function and facial emotional perception in the early recognition and intervention of schizophrenia. These dimensions not only serve as important indicators for early diagnosis of schizophrenia but also show their importance in assessing patient symptom severity. Impairments of cognitive and social functioning, and a decline in facial emotion perceptual processing, together constitute the biological and behavioral markers of early recognition in schizophrenia. Through the application of knowledge management strategies, such as patient education, family support, and integration of community resources, one can optimize treatment paths for patients with schizophrenia and improve treatment outcomes. Meanwhile, through the application of knowledge management strategies, the significant improvement of symptom severity score and quality of life scale scores after receiving comprehensive treatment, further confirmed the effectiveness of these strategies in improving the treatment effect and patient quality of life.

Funding

No. YKSZ001.