Individual and integrated indexes of inflammation predicting the risks of mental disorders - statistical analysis and artificial neural network
Abstract Objective The prevalence of mental illness in Taiwan increased. Identifying and mitigating risk factors for mental illness is essential. Inflammation may be a risk factor for mental illness; however, the predictive power of inflammation test values is unclear. Artificial intelligence can pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
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| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12888-025-06652-3 |
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| Summary: | Abstract Objective The prevalence of mental illness in Taiwan increased. Identifying and mitigating risk factors for mental illness is essential. Inflammation may be a risk factor for mental illness; however, the predictive power of inflammation test values is unclear. Artificial intelligence can predict the risk of disease. This study was the first to conduct risk prediction based on the combination of individual inflammation test values. Methods A retrospective longitudinal design was adopted to analyze data obtained from a medical center. Patients were enrolled if they had received blood tests for inflammation. Propensity score matching was employed for within-group comparisons. A total of 231,306 patients were enrolled. A deep neural network model was employed to establish a predictive model. Results Among inflammation markers, high-sensitivity C-reactive protein concentrations were associated with the greatest risk of mental illness (37.45%), followed by the combination of individual inflammation test values (32.21%). The more abnormal a participant’s inflammation values were, the higher the risk of mental illness (aHR = 1.301, p <.001). Specifically, high-sensitivity C-reactive protein concentration was the most indicative marker for predicting mental illness. Inflammation markers exhibited certain correlations with the type of mental illness. When the same variables were considered, statistical analysis and the deep neural network had similar results. After feature extraction was incorporated, the performance of the deep neural network model improved (excellent, area under the curve = 0.9162) and could effectively predict the risk of mental illness. Conclusion Inflammation values could predict the risk of developing mental illnesses in general and the risk of developing certain types of mental illness. |
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| ISSN: | 1471-244X |