Intensivists

When distress meets technology: The mediating role of AI integration in the link between nurses' moral distress and moral integrity in critical care settings.

BACKGROUND: Moral distress is increasingly recognized as a critical challenge in critical care settings, where nurses frequently encounter ethically complex situations that can undermine their moral integrity. With the growing adoption of artificial intelligence (AI) in clinical decision-making, there is a need to understand whether AI integration may help mitigate the ethical burden experienced b...

Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified-Delphi Consensus.

BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) demonstrate performance comparable or superior to human experts in certain tasks. However, their integration into surgical practice faces a significant implementation gap, alongside ethical, privacy, and legal concerns. Clear governance frameworks are needed to guide their responsible adoption in surgery, to ...

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, part...

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury.

Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have mad...

Evaluation of oxygenation indices incorporating SpO₂ and PEEP for assessing ARDS severity: Evidence from the MIMIC-IV and eICU collaborative research database v2.0 databases.

BACKGROUND: The in-hospital mortality of acute respiratory distress syndrome can reach 35-45%, with patients requiring a more convenient and accurate ...

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models.

BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We ...

Iatrogenic plasticizer Di(2-ethylhexyl) phthalate (DEHP) exposure increases Sepsis mortality risk: Machine learning implicates monocyte-driven immune dysregulation.

Sepsis poses a significant global health burden, and ICU patients are disproportionately exposed to di(2-ethylhexyl) phthalate (DEHP), an immunotoxic ...

Impacts of delayed influenza vaccination on clinical outcomes in ICU-admitted patients with influenza: A retrospective cohort study.

BACKGROUND: Influenza vaccination is an effective measure for reducing the risk of severe influenza infection. However, the task of achieving adequate...

Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.

BACKGROUND: Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosi...

Association of lactate-to-albumin ratio with 28-day mortality in elderly sepsis patients: a retrospective MIMIC-IV database analysis.

BACKGROUND: The lactate-to-albumin ratio (LAR) is correlated with mortality in critically ill patients; however, its predictive value for sepsis in th...

Early heart-rate trajectory phenotypes predict short-term mortality in critically ill patients: a dynamic time-warping cluster analysis.

UNLABELLED: Heart rate (HR) reflects illness severity in critically ill patients, but the prognostic significance of early HR changes is unclear. We a...

K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making ...

Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.

BACKGROUND: Conventional machine learning (ML) models for predicting surgical outcomes have limitations in generalizability We explored large language...

Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.

BACKGROUND: Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital h...

An Explanation User Interface for Artificial Intelligence-Supported Mechanical Ventilation Optimization for Clinicians: User-Centered Design and Formative Usability Study.

BACKGROUND: The integration of artificial intelligence (AI) into clinical decision support systems (CDSSs) for mechanical ventilation in intensive car...

Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine learning algorithms to predict the risk of dementia...

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for general ward patients. This study aimed to identi...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming ...