Intensivists

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were u...

Prognostic value of HSP27 in 28-day mortality in septic ICU patients: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the association between serum heat shock protein 27 (HSP27) levels and 28-day mortality in patients with sepsis.

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Inten...

Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with seps...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators a...

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the po...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including admini...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving o...

EC and EC of Remifentanil for Inhibiting Bronchoscopy Responses in Elderly Patients During Fiberoptic Bronchoscopy Under Ciprofol Sedation: An Up-and-Down Sequential Allocation Trial.

BACKGROUND: Opioids are used to suppress cough during fiberoptic bronchoscopy (FOB). However, evidence regarding the optimal dose of remifentanil duri...

Characteristics and outcomes of pulmonary barotrauma in patients with COVID-19 ARDS: A retrospective observational study.

INTRODUCTION: Pulmonary barotrauma in coronavirus disease-2019 (COVID-19) acute respiratory distress syndrome (ARDS) carries high risk of mortality. W...

Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach.

This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma sa...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL...

Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.

BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of ...

An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for pat...

Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.

Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with...

The Hemodynamic Management and Postoperative Outcomes After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Prospective Observational Study.

Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has become standard treatment for peritoneal cancers and metastases,...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a cas...