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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.23491 |
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| _version_ | 1866911230703173632 |
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| author | Vershinina, Olga Sabbatinelli, Jacopo Bonfigli, Anna Rita Colombaretti, Dalila Giuliani, Angelica Krivonosov, Mikhail Trukhanov, Arseniy Franceschi, Claudio Ivanchenko, Mikhail Olivieri, Fabiola |
| author_facet | Vershinina, Olga Sabbatinelli, Jacopo Bonfigli, Anna Rita Colombaretti, Dalila Giuliani, Angelica Krivonosov, Mikhail Trukhanov, Arseniy Franceschi, Claudio Ivanchenko, Mikhail Olivieri, Fabiola |
| contents | Objective. Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies. Research Design and Methods. This study analyzed a cohort of 554 patients (aged 40-87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model. Results. The extra survival trees (EST) model, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model's individual decision-making processes. Conclusions. The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus Vershinina, Olga Sabbatinelli, Jacopo Bonfigli, Anna Rita Colombaretti, Dalila Giuliani, Angelica Krivonosov, Mikhail Trukhanov, Arseniy Franceschi, Claudio Ivanchenko, Mikhail Olivieri, Fabiola Machine Learning Objective. Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies. Research Design and Methods. This study analyzed a cohort of 554 patients (aged 40-87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model. Results. The extra survival trees (EST) model, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model's individual decision-making processes. Conclusions. The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization. |
| title | Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.23491 |