Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910961170907136 |
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| author | Si, Yong Fan, Junyi Sun, Li Chen, Shuheng Pishgar, Elham Alaei, Kamiar Placencia, Greg Pishgar, Maryam |
| author_facet | Si, Yong Fan, Junyi Sun, Li Chen, Shuheng Pishgar, Elham Alaei, Kamiar Placencia, Greg Pishgar, Maryam |
| contents | Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and accurate prediction of mortality is essential for guiding clinical decision-making and optimizing ICU resource allocation. In this study, we utilized the MIMIC-III database to identify geriatric TBI patients and applied a machine learning framework to develop a 30-day mortality prediction model. A rigorous preprocessing pipeline-including Random Forest-based imputation, feature engineering, and hybrid selection-was implemented to refine predictors from 69 to 9 clinically meaningful variables. CatBoost emerged as the top-performing model, achieving an AUROC of 0.867 (95% CI: 0.809-0.922), surpassing traditional scoring systems. SHAP analysis confirmed the importance of GCS score, oxygen saturation, and prothrombin time as dominant predictors. These findings highlight the value of interpretable machine learning tools for early mortality risk stratification in elderly TBI patients and provide a foundation for future clinical integration to support high-stakes decision-making in critical care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Si, Yong Fan, Junyi Sun, Li Chen, Shuheng Pishgar, Elham Alaei, Kamiar Placencia, Greg Pishgar, Maryam Quantitative Methods Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and accurate prediction of mortality is essential for guiding clinical decision-making and optimizing ICU resource allocation. In this study, we utilized the MIMIC-III database to identify geriatric TBI patients and applied a machine learning framework to develop a 30-day mortality prediction model. A rigorous preprocessing pipeline-including Random Forest-based imputation, feature engineering, and hybrid selection-was implemented to refine predictors from 69 to 9 clinically meaningful variables. CatBoost emerged as the top-performing model, achieving an AUROC of 0.867 (95% CI: 0.809-0.922), surpassing traditional scoring systems. SHAP analysis confirmed the importance of GCS score, oxygen saturation, and prothrombin time as dominant predictors. These findings highlight the value of interpretable machine learning tools for early mortality risk stratification in elderly TBI patients and provide a foundation for future clinical integration to support high-stakes decision-making in critical care. |
| title | Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2505.15850 |