Critical Care

Stacking machine learning model for risk stratification of acute respiratory distress syndrome after traumatic brain injury: a multicenter retrospective study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication after traumatic brain injury (TBI), and early risk stratification may facilitate timely preventive interventions and improve clinical outcomes. METHODS: Model development used the MIMIC-IV v3.1 database with external validation in a cohort from the First Affiliated Hospital of Xinjiang Medical University. Candidate var...

YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications.

Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health ri...

Development and validation of a machine learning-based prediction model for malnutrition risk in peritoneal dialysis patients: a multi‑center retrospective study.

PURPOSE: To develop and validate a machine learning (ML)‑based model for predicting malnutrition risk in patients undergoing maintenance peritoneal di...

Using the Machine Learning Algorithm XGBoost to Understand the Correlates of Wearing Hearing Protection in Central States' Agricultural Producers.

PURPOSE: Hearing loss is a common work-related condition. Estimates suggest that 46%-86% of agricultural workers have hearing loss. Agricultural safet...

Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.

Sepsis is a high-burden, highly heterogeneous clinical challenge that affects up to 30% of ICU patients. Reliable early prediction is essential for ti...

Predicting ICU in-hospital mortality from text-encoded structured EHR data using adaptive transformer layer fusion.

Early identification of ICU patients at high mortality risk is essential for triage and timely intervention. We present adaptive layer fusion with int...

An interpretable machine learning model for preoperative risk stratification in paediatric congenital heart disease surgery: a retrospective cohort study.

BACKGROUND: Congenital heart disease (CHD), one of the most common birth defects, poses challenges to preoperative risk stratification due to its anat...

Symptom burden in dialysis patients: determinants and impact on mortality.

BACKGROUND: Symptom burden in chronic kidney disease (CKD) patients has a negative impact on functional status and quality of life. Despite the high p...

Results of a new model of videotraining for patients and caregivers for peritoneal dialysis: food for (AI) thought?

BACKGROUND: In an attempt to overcome the space-time limitations of traditional training we used a new telemedicine home-training model (Videotraining...

Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.

Predicting the outcome of comatose patients in the intensive care unit (ICU) can inform decision making but remains challenging. Recent studies sugges...

Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. T...

Multi-omics biomarkers in endometrial receptivity: from mechanisms to clinical translation.

BACKGROUND: Endometrial receptivity (ER) serves as a critical determinant for successful embryo implantation, yet its molecular complexity and limited...

Operational Integration and Temporal Validation of a Continuously Deployed ICU Prediction Model.

OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluatio...

Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.

BACKGROUND: Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary disease, particularly in low- and middle-income countrie...

The use of logic for machine learning models in sepsis.

Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.

BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the ...

A data-informed multidimensional composite score for stress assessment.

Occupations such as the military, emergency responders, and athletes demand acute physical, cognitive and psychosocial effort. Individuals in these ro...

ICU command centers and evidence-based artificial intelligence - Response to Madeo et al.

Adaptive spatiotemporal graph learning for multi-horizon probabilistic wind power forecasting.

Accurate and reliable forecasting of wind power generation is a cornerstone for ensuring the operational flexibility, economic efficiency, and grid st...

Integration of single-cell and bulk RNA sequencing reveals programmed cell death-associated transcriptional programs in sepsis-induced acute lung injury.

BACKGROUND: Sepsis-induced acute lung injury (ALI) is a frequent and life-threatening complication of sepsis, yet clinically actionable transcriptomic...