Early warning mechanisms and intervention strategies based on dynamic negative emotion prediction models for physiopathological risks in cancer patients

Article ID: 5075

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Keywords: cancer patients; dynamic evolution; early warning mechanism; machine learning; negative emotions; personalized intervention; physiological and pathological risk; prediction model

Abstract

Objective: To construct a dynamic evolution-based negative emotion prediction model, establish an early warning mechanism for physiological and pathological risks in cancer patients, and develop personalized psychological intervention strategies. Methods: A multistage mixed-methods design was employed, conducting a 12-month longitudinal follow-up study of 480 cancer patients. Standardized instruments, including the Hospital Anxiety and Depression Scale, Fear of Progression Questionnaire-Short Form, and Perceived Stress Scale, were used to assess negative emotions, while physiological indicators, including inflammatory markers, immune function parameters, and endocrine parameters, were simultaneously measured. Machine learning algorithms, including Random Forest, Support Vector Machine, Gradient Boosting Decision Tree, and Deep Neural Networks, were utilized to construct prediction models, with model performance evaluated through 10-fold cross-validation and external validation. Personalized psychological intervention strategies were developed based on prediction results, and intervention effectiveness was assessed using randomized controlled trials. Results: Negative emotions in cancer patients exhibited a three-stage dynamic evolution pattern of “impact-adaptation-integration,” with anxiety, depression, and fear of disease progression scores reaching peak levels in early treatment phases before gradually declining. Environmental factors such as social support, family functioning, and medical environment, along with individual difference variables including age, gender, and personality traits, significantly moderated emotional evolution. The ensemble prediction model achieved an AUC value of 0.892 (95% CI: 0.876–0.908) in internal validation and 0.876 (95% CI: 0.845–0.907) in external validation, with a sensitivity of 85.4% and specificity of 83.7%. The three-tier risk stratification early warning system achieved an optimal balance at the moderate risk threshold, with a Youden index of 0.691. Personalized psychological intervention strategies significantly improved patients’ psychological symptoms, physiological indicators, and quality of life. The intervention group showed 34.1% improvement in anxiety scores, 38.1% improvement in depression scores, 38.1% reduction in C-reactive protein levels, and 24.7% enhancement in quality-of-life scores, all significantly superior to the control group (p < 0.001). Path analysis revealed that personalized interventions primarily operated through five mediating variables: cognitive restructuring, emotion regulation, coping strategies, social support utilization, and self-efficacy, with a total indirect effect of 0.71, accounting for 78.9% of the total effect. Conclusion: The dynamic evolution-based negative emotion prediction model demonstrates excellent predictive performance and clinical application value. The established early warning mechanism effectively identifies high-risk patients, and personalized psychological intervention strategies significantly improve patients’ multidimensional health outcomes, providing a scientific theoretical foundation and practical guidance for psychological health management in cancer patients.