ArticleScientific reports2023
Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Risk prediction models for delirium in ICU patients: a systematic review and critical appraisal.BMC anesthesiology · 2025Pooled it
- Systematic review of risk prediction models for sepsis-associated brain dysfunction.Frontiers in neurologyPooled it
- Development and exploratory external assessment of a machine learning model for early delirium prediction in non-mechanically ventilated patients with sepsis.Clinical and experimental medicine · 2026Article
- Predicting ICU Delirium with a Regularized Logistic Regression Model: Device-Support Burden as an Informative Predictor Domain in a Turkish Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- Clinical Significance of Serum Soluble Klotho Levels in Sepsis-Associated Encephalopathy: A Single-Center Prospective, Preliminary Study.Journal of clinical medicine · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Clinical clusters during acute illness predict long-term mortality in older patients.BMC medicine · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.Renal failure · 2025Observational
- Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.European journal of medical research · 2025Article
- Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease.BMC infectious diseases · 2025Article
- Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.Health science reports · 2025Article
- Prognostic Value of the Charlson Comorbidity Index for Mortality and Machine Learning-Based Prediction in Critically Ill Patients with Paralytic Ileus: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Article
- Association between albumin-corrected anion gap and delirium in acute pancreatitis: insights from the MIMIC-IV database.BMC gastroenterology · 2025Article
- Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.Journal of medical Internet research · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Factors linked to lung cancer in MIMIC-IV database.Journal of thoracic disease · 2025Article
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Authors and funding
6 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods.
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