Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms
ObjectiveEarly assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study aims to develop an optimally performing mortality risk prediction model for AIDS patients with comorbid AIDS-related diseases or symptoms to facilitate...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1544351/full |
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| author | Yiwei Chen Kejun Pan Xiaobo Lu Erxiding Maimaiti Maimaitiaili Wubuli |
| author_facet | Yiwei Chen Kejun Pan Xiaobo Lu Erxiding Maimaiti Maimaitiaili Wubuli |
| author_sort | Yiwei Chen |
| collection | DOAJ |
| description | ObjectiveEarly assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study aims to develop an optimally performing mortality risk prediction model for AIDS patients with comorbid AIDS-related diseases or symptoms to facilitate early intervention.MethodsThe study included 478 first-time hospital-admitted AIDS patients with related diseases or symptoms. Eight predictors were screened using lasso regression, followed by building eight models and using SHAP values (Shapley’s additive explanatory values) to identify key features in the best models. The accuracy and discriminatory power of model predictions were assessed using variable importance plots, receiver operating characteristic curves, calibration curves, and confusion matrices. Clinical benefits were evaluated through decision-curve analyses, and validation was performed with an external set of 48 patients.ResultsLasso regression identified eight predictors, including hemoglobin, infection pathway, Sulfamethoxazole-Trimethoprim, expectoration, headache, persistent diarrhea, Pneumocystis jirovecii pneumonia, and bacterial pneumonia. The optimal model, XGBoost, yielded an Area Under Curve (AUC) of 0.832, a sensitivity of 0.703, and a specificity of 0.799 in the training set. In the test set, the AUC was 0.729, the sensitivity was 0.717, and the specificity was 0.636. In the external validation set, the AUC was 0.873, the sensitivity was 0.852, and the specificity was 0.762. Furthermore, the calibration curves showed a high degree of fit, and the DCA curves demonstrated the overall high clinical utility of the model.ConclusionIn this study, an XGBoost-based mortality risk prediction model is proposed, which can effectively predict the mortality risk of patients with co-morbid AIDS-related diseases or symptomatic AIDS, providing a new reference for clinical decision-making. |
| format | Article |
| id | doaj-art-93f64bea4fa24e8495fd0fdba3d09a71 |
| institution | OA Journals |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-93f64bea4fa24e8495fd0fdba3d09a712025-08-20T02:04:28ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-03-011310.3389/fpubh.2025.15443511544351Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptomsYiwei Chen0Kejun Pan1Xiaobo Lu2Erxiding Maimaiti3Maimaitiaili Wubuli4Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, ChinaObjectiveEarly assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study aims to develop an optimally performing mortality risk prediction model for AIDS patients with comorbid AIDS-related diseases or symptoms to facilitate early intervention.MethodsThe study included 478 first-time hospital-admitted AIDS patients with related diseases or symptoms. Eight predictors were screened using lasso regression, followed by building eight models and using SHAP values (Shapley’s additive explanatory values) to identify key features in the best models. The accuracy and discriminatory power of model predictions were assessed using variable importance plots, receiver operating characteristic curves, calibration curves, and confusion matrices. Clinical benefits were evaluated through decision-curve analyses, and validation was performed with an external set of 48 patients.ResultsLasso regression identified eight predictors, including hemoglobin, infection pathway, Sulfamethoxazole-Trimethoprim, expectoration, headache, persistent diarrhea, Pneumocystis jirovecii pneumonia, and bacterial pneumonia. The optimal model, XGBoost, yielded an Area Under Curve (AUC) of 0.832, a sensitivity of 0.703, and a specificity of 0.799 in the training set. In the test set, the AUC was 0.729, the sensitivity was 0.717, and the specificity was 0.636. In the external validation set, the AUC was 0.873, the sensitivity was 0.852, and the specificity was 0.762. Furthermore, the calibration curves showed a high degree of fit, and the DCA curves demonstrated the overall high clinical utility of the model.ConclusionIn this study, an XGBoost-based mortality risk prediction model is proposed, which can effectively predict the mortality risk of patients with co-morbid AIDS-related diseases or symptomatic AIDS, providing a new reference for clinical decision-making.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1544351/fullmachine learningXGBoostAIDSHIVprediction model |
| spellingShingle | Yiwei Chen Kejun Pan Xiaobo Lu Erxiding Maimaiti Maimaitiaili Wubuli Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms Frontiers in Public Health machine learning XGBoost AIDS HIV prediction model |
| title | Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms |
| title_full | Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms |
| title_fullStr | Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms |
| title_full_unstemmed | Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms |
| title_short | Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms |
| title_sort | machine learning based prediction of mortality risk in aids patients with comorbid common aids related diseases or symptoms |
| topic | machine learning XGBoost AIDS HIV prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1544351/full |
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