Intensivists

[Development and validation of an explainable machine learning-based model for predicting mortality risk in polytrauma patients].

Polytrauma is commonly defined as multisystem trauma involving at least two body regions with an Abbreviated Injury Scale (AIS) score ≥ 3, characterized by complex pathophysiological interactions and extreme clinical heterogeneity. This study retrospectively analyzed data from 171 274 polytrauma patients in the National Trauma Data Bank (NTDB) from 2018 to 2021. Key features were selected using th...

ICU Open-Access Databases for Artificial Intelligence in Sepsis: Balancing Innovation With Data Quality Challenges.

OBJECTIVES: To evaluate the suitability of three major open-access ICU databases (Medical Information Mart for Intensive Care IV [MIMIC-IV], eICU Collaborative Research Database [eICU-CRD], and Amsterdam University Medical Center Database [AmsterdamUMCdb]) for artificial intelligence (AI)-based sepsis research, with a focus on data completeness, internal consistency, terminological standardization...

Historians, Not Prophets: Sepsis Temperature Trajectories in the Machine Learning Age.

Multi-omics integration and machine learning define an iron-sulfur cluster/zinc-binding protein prognostic signature in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is characterized by substantial intratumoral heterogeneity and poor clinical prognosis. Although metalloprot...

Artificial count enhancement in lung scintigraphy for acquisition acceleration and pseudo-planar generation.

Ventilation-perfusion (V/Q) planar scintigraphy remains widely used for diagnosing pulmonary embolism (PE), despite the reported superior diagnos...

Machine learning prediction of the change in sleepiness following continuous positive airway pressure in obstructive sleep apnea: a multi-cohort analysis.

STUDY OBJECTIVES: To predict the improvement in sleepiness following continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea ...

Pulmonary function estimation and reliability assessment using hybrid-optimized ensemble learning.

Objective.Pulmonary function tests are central to assessing ventilatory impairment but are often limited by accessibility, repeatability, and patient ...

Correction to "Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach".

[This corrects the article DOI: 10.1002/hsr2.71556.].

Severity scoring systems in intensive care: new models, or continuous recalibration?

PURPOSE OF REVIEW: To review how ICU illness severity scoring systems perform across settings and over time, and when recalibration is sufficient vers...

Early Sepsis Prediction Using Interpretable Models.

Sepsis remains a major cause of morbidity and mortality in Intensive Care Units (ICUs). Timely identification of sepsis can prevent severe complicatio...

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim i...

Barriers and Facilitators to AI Implementation in Intensive Care Units in China: Qualitative Study Among Nurse Managers.

BACKGROUND: AI has shown significant potential in intensive care unit (ICU) nursing practice, enhancing efficiency, decision-making, and patient safet...

Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.

BACKGROUND: The growing population of cancer survivors faces immense monitoring burdens due to rigid follow-up guidelines, such as the intensive surve...

Development and validation of a deep learning model to predict heparin response in critically ill patients with deep vein thrombosis receiving continuous intravenous heparin infusion.

Continuous intravenous heparin infusion is widely used for deep vein thrombosis (DVT) in the intensive care unit (ICU), but accurate prediction of act...

Development and evaluation of a multi-source machine learning model to predict critical illness and death in patients with substance misuse.

OBJECTIVES: Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors....

Clinical Utility of LLM-assisted Chart Review for the Detection of Bleeding Events.

INTRODUCTION: Red blood cell (RBC) transfusions are frequently administered in the intensive care unit (ICU) and are independently associated with inc...

The structural and dynamical stability of BeF2 at finite temperatures studied by machine learning molecular dynamics.

BeF2 is a key component of fluoride coolants in molten salt reactors. The structural phase transition between its α- and β-quartz phases at high tempe...

Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.

PURPOSE: Artificial intelligence (AI) is increasingly being applied in the field of infectious diseases. This study aimed to characterize publication ...

A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.

Delirium occurs frequently in emergency departments and is associated with poor outcomes and increased burden. Early delirium risk prediction is cruci...

Moral distress risk profiling in ICU nurses: A cross-sectional study.

BackgroundMoral distress is common among ICU nurses and has been linked to burnout, diminished care quality, and turnover. Which factors matter most -...