Intensivists

Development and external validation of a machine learning model for predicting post-CABG acute kidney injury: insights from MIMIC database and real-world cardiac surgery cohort.

OBJECTIVE: Acute kidney injury (AKI) is a severe complication following coronary artery bypass grafting(CABG) While machine learning models trained on large-scale intensive care unit (ICU) databases are increasingly prevalent, their ability to generalize to specialized surgical cohorts in real-world settings remains poorly characterized. This study aimed to develop a post-CABG AKI prediction model...

HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data.

BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to specific patient populations. We aimed to develop a simple, interpretable, and broadly applicable risk score to screen for 30-day postoperative mortality using routinely available variables. METHODS: We developed the HeLP-BAG score using three large surg...

Intelligent Multimodal Sensors Based on Two-Dimensional Materials: Fabrication, Decoupling, and Applications.

Two-dimensional (2D) materials, with their multi-stimulus responsiveness and excellent electrical/mechanical properties, have accelerated sensor techn...

A Multifunctional Eutectogel for Wide-Temperature and Intelligent Speech Recognition.

The practical application of flexible sensors is often constrained by limited mechanical properties and a narrow operating temperature range, particul...

Acute brain dysfunction clusters in COVID-19: a pilot machine learning-based analysis of the COVID-D cohort.

PURPOSE: While acute brain dysfunction (ABD, i.e., delirium and coma) is associated with significantly increased morbidity in critically ill patients,...

Machine learning-based prediction of 30-day mortality in critically ill patients with rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis patients in the ICU face a high risk of mortality. While traditional ICU scoring systems are not specifically designe...

Machine learning methods identified cellular senescence-related hub molecules in sepsis-induced acute respiratory distress syndrome (ARDS) and their upstream regulatory network.

BACKGROUND: Sepsis-induced ARDS demonstrated greater severity and higher mortality compared to ARDS triggered by other factors. In this article, we co...

An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.

The aim of this study is to develop and validate a machine learning-based predictive model to assess the risk of acquired bloodstream infection (BSI) ...

Association of Staphylococcus aureus bloodstream infection with 7-day incident delirium in critically Ill adults: a propensity-weighted competing risk analysis.

BACKGROUND: Delirium is a frequent manifestation of acute brain dysfunction in critically ill patients with bloodstream infections (BSI). While the as...

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 ...

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...

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...

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 ...

Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm.

BACKGROUND: The pathological heterogeneity of sepsis makes it challenging for traditional scoring systems to balance early-warning sensitivity, dynami...

Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.

OBJECTIVE: Given the high alignment between deep learning and blended teaching objectives, blended teaching provides a feasible pathway for achieving ...