ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Schizophrenia
Artificial Intelligence-Based Diagnostic Model for Schizophrenia in Individuals Living with HIV
Junzhi Chen 1
Tongping Ren 2
Jianjian Li 3
Meilin Li 3
Xiongjun Li 3
Chunyang Hu 4
Zhongliang Jiang 3
Xiaolin He 3
Youwang Lu 3
1. College of nursing, Dali University, Dali, China
2. School of Public Health, Kunming Medical University, Kunming, China
3. Yunnan Provincial Hospital of Infectious Diseases, Kunming, China
4. School of Public Health, Dali University, Dali, China
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Abstract
Background: Schizophrenia is one of the most prevalent severe mental disorders among people living with HIV (PLWH). Delayed diagnosis and misdiagnosis contribute to poor prognosis and substantial economic burden in this population. However, there are currently no validated diagnostic models available for schizophrenia in PLWH. Methods: PLWH attending annual follow-ups at Yunnan Provincial Hospital of Infectious Diseases/Yunnan AIDS Care Center were enrolled. Hematological parameters were compared between PLWH with schizophrenia (HIV-Scz) and those without (HIV-non-Scz) and diagnostic models were constructed using six machine learning algorithms. Model performance was evaluated comprehensively using area under the curve (AUC), accuracy, F1 score, recall, precision, and decision curve analysis. SHapley Additive exPlanations (SHAP) were applied to determine the relative importance of each feature. Results: A total of 186 participants were included in this study, including 62 with clinically confirmed schizophrenia who were receiving antipsychotic treatment at the time of blood sampling. Compared with the HIV-non-Scz group, the HIV-Scz group exhibited significant differences across multiple hematological parameters. Six machine learning models constructed using 28 routine blood parameters demonstrated diagnostic capability, among which the Lasso regression model achieved the best overall performance, with mean AUC (0.966 ± 0.016), F1-score (0.839 ± 0.067), and accuracy (0.897 ± 0.037), together with favorable precision (0.867 ± 0.061) and recall (0.821 ± 0.111). Decision curve analysis indicated that this model provided a higher net benefit within clinically relevant threshold probability ranges. Furthermore, SHAP analysis identified PDW, MPV and MCV as the most influential features contributing to model predictions. Conclusion: Routine hematological parameters may serve as potential diagnostic biomarkers for schizophrenia in PLWH, although medication-related effects in treated patients cannot be excluded.
Summary
Keywords
diagnosticmodel, hematological parameters, hiv/aids, machine learning, Schizophrenia
Received
21 September 2025
Accepted
19 February 2026
Copyright
© 2026 Chen, Ren, Li, Li, Li, Hu, Jiang, He and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhongliang Jiang; Xiaolin He; Youwang Lu
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