ArticleFrontiers in neurology2023
Factor analysis based on SHapley Additive exPlanations for sepsis-associated encephalopathy in ICU mortality prediction using XGBoost - a retrospective study based on two large database.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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19 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Clinical Significance of Serum Soluble Klotho Levels in Sepsis-Associated Encephalopathy: A Single-Center Prospective, Preliminary Study.Journal of clinical medicine · 2026Article
- Machine learning-based prediction of rotator cuff tears using anatomical parameters: a retrospective cohort study.BMC musculoskeletal disorders · 2026Article
- Machine learning-based mortality prediction models for emergency department patients: a comparative analysis.Frontiers in medicine · 2026Article
- Online Clinical Calculator for Predicting 28-Day Mortality in Older Adult Patients With Sepsis-Associated Encephalopathy: Retrospective Study Using MIMIC-IV.JMIR medical informatics · 2025Article
- Interpretable machine learning-based prediction of mortality in critical cancer patients with delirium: A retrospective cohort study.Asia-Pacific journal of oncology nursing · 2025Article
- CatLet Score as a Predictor of Cardiac Death in Patients With Acute Myocardial Infarction: Insights From Interpretable Machine Learning Models.Reviews in cardiovascular medicine · 2025Article
- Combining radiomics of X-rays with patient functional rating scales for predicting satisfaction after radial fracture fixation: a multimodal machine learning predictive model.BMC musculoskeletal disorders · 2025Article
- Central Thalamic Deep Brain Stimulation Modulates Autonomic Nervous System Responsiveness in Disorders of Consciousness.CNS neuroscience & therapeutics · 2025Article
- Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2025Article
- Early mortality prediction after severe trauma using ensemble machine learning: a single-center retrospective study.Frontiers in public health · 2025Article
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- Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy.Scientific reports · 2024Article
- Characteristics and risk factors for invasive fungal infection in hospitalized patients with acute-on-chronic hepatitis B liver failure: a retrospective cohort study from 2010 to 2023.Frontiers in microbiology · 2024Article
- Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.Frontiers in cellular and infection microbiology · 2024Article
- Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO-XGBoost-SHAP framework.Digital healthArticle
- Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Sepsis-associated encephalopathy (SAE) is strongly linked to a high mortality risk, and frequently occurs in conjunction with the acute and late phases of sepsis. The objective of this study was to construct and verify a predictive model for mortality in ICU-dwelling patients with SAE. Methods: The study selected 7,576 patients with SAE from the MIMIC-IV database according to the inclusion criteria and randomly divided them into training ( Results: The survival rate of patients with SAE in the MIMIC-IV database was 88.6% and that of 1,573 patients in the eICU-CRD database was 89.1%. The ROC of the XGBoost model indicated good discrimination. The AUCs for the training, test, and validation sets were 0.908, 0.898, and 0.778, respectively. The impact of each parameter on the XGBoost model was depicted using a SHAP plot, covering both positive (acute physiology score III, vasopressin, age, red blood cell distribution width, partial thromboplastin time, and norepinephrine) and negative (Glasgow Coma Scale) ones. Conclusion: A prediction model developed using XGBoost can accurately predict the ICU mortality of patients with SAE. The SHAP approach can enhance the interpretability of the machine-learning model and support clinical decision-making.
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