AIMC Topic: Intensive Care Units
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Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.
Scientific reports
Jul 2, 2025
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...
Development and validation of a modified SOFA score for mortality prediction in candidemia patients.
Scientific reports
Jul 1, 2025
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...
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
Jun 19, 2025
BACKGROUND: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications.
Development and validation of a cardiac surgery-associated acute kidney injury prediction model using the MIMIC-IV database.
PloS one
Jun 12, 2025
OBJECTIVE: This study aimed to develop an innovative early prediction model for acute kidney injury (AKI) following cardiac surgery in intensive care unit (ICU) settings, leveraging preoperative and postoperative clinical variables, and to identify k...
Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.
Journal of medical Internet research
Jun 11, 2025
BACKGROUND: Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.
Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.
Scientific reports
Jun 4, 2025
Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients...
Mortality Prediction Performance Under Geographical, Temporal, and COVID-19 Pandemic Dataset Shift: External Validation of the Global Open-Source Severity of Illness Score Model.
Critical care explorations
Jun 4, 2025
BACKGROUND: Risk-prediction models are widely used for quality of care evaluations, resource management, and patient stratification in research. While established models have long been used for risk prediction, healthcare has evolved significantly, a...
A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.
Renal failure
Jun 2, 2025
Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in ...
AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.
BMC medical informatics and decision making
Jun 2, 2025
BACKGROUND: Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the ex...
Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.
Nutrients
May 31, 2025
BACKGROUND: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to hea...
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