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References from Development and validation of an interpretable machine-learning model for predicting treatment failure in severe trauma patients. Local targets link to admitted publications; unresolved targets remain external evidence.
Comparative prognostic value of the national institutes of health stroke scale (NIHSS) and the Glasgow coma scale (GCS) in supratentorial and infratentorial stroke patients in western India
10.7759/cureus.65778 · 2024 · External reference
Trauma, a matter of the heart-molecular mechanism of post-traumatic cardiac dysfunction
10.3390/ijms22020737 · 2021 · External reference
The predictive value of the lactate/albumin ratio combined with APACHE II and injury severity score for short-term prognosis in patients with polytrauma
10.1186/s40001-025-03100-6 · 2025 · External reference
Prognostic indicators in patients with isolated thoracic trauma: a retrospective cross-sectional study
10.14744/tjtes.2024.15003 · 2024 · External reference
Using machine learning approaches for multi-omics data analysis: a review
10.1016/j.biotechadv.2021.107739 · 2021 · External reference
Development of a machine learning model to predict cardiac arrest during transport of trauma patients
10.1272/jnms.jnms.2023_90-206 · 2023 · External reference
ICD-10 based machine learning models outperform the trauma and injury severity score (TRISS) in survival prediction
10.1371/journal.pone.0276624 · 2022 · External reference
Identifying age-specific risk factors for poor outcomes after trauma with machine learning
10.1016/j.jss.2023.12.016 · 2024 · External reference
Correlation between clinical findings at admission and Glasgow outcome scale score in children with traumatic brain injury
10.1016/j.wneu.2023.04.121 · 2023 · External reference
Evaluation and analysis of incidence and risk factors of lower extremity venous thrombosis after urologic surgeries: a prospective two-center cohort study using LASSO-logistic regression
10.1016/j.ijsu.2021.105948 · 2021 · External reference
Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus
10.1016/j.ecoenv.2025.118569 · 2025 · External reference
SHAP-Driven Feature analysis approach for epileptic seizure prediction
10.1007/s10916-025-02211-1 · 2025 · External reference
Development of a novel neurological score combining GCS and FOUR scales for assessment of neurosurgical patients with traumatic brain injury: GCS-FOUR scale
10.1016/j.wneu.2023.12.064 · 2024 · External reference
Glasgow Coma scale compared to other trauma scores in discriminating in-hospital mortality of traumatic brain injury patients admitted to urban Indian hospitals: a multicentre prospective cohort study
10.1016/j.injury.2022.09.035 · 2023 · External reference
Development and validation of a pediatric model predicting trauma-related mortality
10.1186/s12887-023-04437-9 · 2023 · External reference
Elevated monocyte distribution width in trauma: an early cellular biomarker of organ dysfunction
10.1016/j.injury.2021.11.026 · 2022 · External reference
Evaluation of mortality prediction using SOFA and APACHE IV tools in trauma and non-trauma patients admitted to the ICU
10.1186/s40001-022-00822-9 · 2022 · External reference
Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit
10.1186/s12876-025-03723-3 · 2025 · External reference
Machine learning-based 28-day mortality prediction model for elderly neurocritically ill patients
10.1016/j.cmpb.2025.108589 · 2025 · External reference
Association of creatine kinase with Alzheimer’s disease pathology: a cross-sectional study
10.1097/cm9.0000000000002773 · 2024 · External reference
Creatine metabolism: energy homeostasis, immunity and cancer biology
10.1038/s41574-020-0365-5 · 2020 · External reference
Ligustilide covalently binds to Cys254 of the creatine kinase, M-type protein, ameliorating acute myocardial ischemia by enhancing the creatine phosphate level
10.1016/j.phymed.2025.156532 · 2025 · External reference
