Intensivists

Comparison of population pharmacokinetic modeling and machine learning approaches for predicting voriconazole trough concentrations in critically ill patients.

BACKGROUND: Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to suboptimal efficacy, especially in intensive care unit (ICU) patients. Machine learning (ML), an artificial intelligence modeling approach, is increasingly being applied to personalized medicine. The effectiveness of ML models for predicting voriconaz...

The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.

INTRODUCTION: Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through antibacterial effects, chemotaxis, and increased vascular permeability. The role of heparin-binding protein in sepsis has been progressively demonstrated, but few studies have been conducted in the context of polytrauma combined with bacterial infec...

Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects t...

Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study.

BACKGROUND: Artificial intelligence (AI) has been widely adopted for the prediction of latent shock occurrence in critically ill patients in intensive...

Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

BACKGROUND: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicti...

Predictive modeling of ICU-AW inflammatory factors based on machine learning.

BACKGROUND: ICU-acquired weakness (ICU-AW) is a common complication among ICU patients. We used machine learning techniques to construct an ICU-AW inf...

Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer.

This study examined the characteristics of the broad model (KBP) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) pl...

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate id...

Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.

BACKGROUND: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for ...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing earl...

Spontaneous Hepatic Rupture Complicating Preeclampsia and HELLP Syndrome: A Case Report.

Spontaneous hepatic rupture is a rare complication that occurs in pregnant mothers with HELLP syndrome, or preeclampsia with severe features, or ecla...

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in ...

Multibody system dynamics for bio-robotic design and simulation based on inching-locomotion caterpillar's gait: MBD-ILAR method.

Inching-locomotion caterpillars (ILAR) inspire the design of 'inch-worm' robots with biomimicry features, that can be adapted to different environment...

Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).

INTRODUCTION: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challen...

Integrated multi-omics analysis describes immune profiles in ischemic heart failure and identifies PTN as a novel biomarker.

INTRODUCTION: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF) being a major contributor. IHF is primarily driv...

Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney rep...

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively ...

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensembl...

M4Net: Multi-level multi-patch multi-receptive multi-dimensional attention network for infrared small target detection.

The detection of infrared small targets is getting more and more attention, and has a wider application in both military and civilian fields. The trad...