Intensivists

Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.

OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI are crucial.

Deep learning for fetal inflammatory response diagnosis in the umbilical cord.

INTRODUCTION: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute feta...

Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis.

Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein,...

Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.

BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU...

Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with se...

Host Biomarkers and Antibiotic Tissue Penetration in Sepsis: Insights from Moxifloxacin.

BACKGROUND AND OBJECTIVE: Sepsis-induced pathophysiological changes may lead to pharmacokinetic variability which alters antibiotic concentrations at ...

[Acute respiratory distress syndrome-quo vadis : Innovative and individualized treatment approaches].

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome characterized by variable pathophysiology and different therapeutic ap...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early i...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of p...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to ...

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the tem...

AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o.

This study examines GPT-4o's ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malig...

Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, ...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential gene...

Revolutionizing Intensive Care Unit Care: A Scoping Review of Multimodal Family Engagement Technologies.

This scoping review systematically examines the emerging field of multimodal family engagement technologies in intensive care units (ICUs). Despite si...

External validation and application of risk prediction model for ventilator-associated pneumonia in ICU patients with mechanical ventilation: A prospective cohort study.

BACKGROUND: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable...

An innovative underdriven multi-degree-of-freedom sea turtle hydrofoil design.

This study presents a new design for a multi-degree-of-freedom underdriven mechanism. The aim is to achieve efficient bionic motion of a sea turtle hy...

Artificial Intelligence for the Detection of Patient-Ventilator Asynchrony.

Patient-ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient...