ArticleIntensive care medicine experimental2025
Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries.
Article in Intensive care medicine experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.BMC medical informatics and decision making · 2026Pooled it
- Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High-Risk Individuals.Health science reports · 2026Article
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.BMC medical informatics and decision making · 2026Article
- Mortality Prediction Among People Living With HIV on Antiretroviral Therapy in Public Health Facilities in Gondar City Administration, Northwest Ethiopia: Machine Learning-Based Study.JMIR medical informatics · 2026Article
- From Narrative to Navigation-A Translational Roadmap for AI-enabled Early Sepsis Prediction: Comment on Shanmugam et al.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Article
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Authors and funding
5 authors.
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Abstract
introductionMortality in sepsis patients remains a challenging condition due to its complex nature. It is an even more prevalent health problem in low- and middle-income countries demanding costly treatment and management. This study proposes an explainable artificial intelligence-based approach towards mortality prediction for patients with sepsis admitted to intensive care unit (ICU).
methodsA total of 500 patients (N = 500, male: female = 262:238, age = 45.96 ± 20.92 years) with sepsis were analyzed retrospectively. We utilize SHapley Additive exPlanations (SHAP) method to gain insights into the preliminary model's learnings regarding the wide array of demographic, clinical, radiological, and laboratory features. The clinical insights were used for feature selection to fetch the top t = 80% feature spread as well as to derive empirical findings from feature dependence plots which could find application in periphery hospital settings. Four machine learning algorithms, Random Forest, XGBoost, Extra Trees and Gradient Boosting classifiers were trained for the binary classification task (discharge from ICU and death in ICU) with the selected influential feature set.
resultsThe Extra Trees Classifier showed the best overall performance with AUROC score: 0.87 (95% CI 0.80-0.93), Accuracy: 0.79 (95% CI 0.71-0.86), F1 score: 0.78 (95% CI 0.69-0.86), Precision: 0.88 (95% CI 0.78-0.98) and Recall: 0.70 (95% CI 0.57-0.82). All four models perform significantly well on dataset with AUROC scores ranging from 0.81 (CI 0.73-0.89) to 0.87 (CI 0.80-0.93) and F1 scores ranging 0.74 (CI 0.64-0.83) to 0.78 (CI 0.69-0.86) on the hold-out test set and were stable over fivefold cross-validation prior to testing.
conclusionsThe proposed approach could provide preemptive estimations into prognostication and outcome prediction of patients with sepsis in low-resource settings. This will aid in clinical decision-making, resource allocation and research for new treatment modalities.
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