Intensivists

Towards the Common Data Model for an Intensive Medicine Data Space in Europe.

This work aims to identify the Key Research Areas for building and deploying the semantic interoperability framework for the Intensive Medicine Data Space in Europe. A set of European experts defined four research areas and associated challenges: i) to characterize the value of Common Data Models, vocabularies and standardized data elements for creating the foundation for the standardization of In...

Medication Recommender System for ICU Patients Using Autoencoders.

Patients admitted to the intensive care unit (ICU) are often treated with multiple high-risk medications. Over- and underprescribing of indicated medications, and inappropriate choice of medications frequently occur in the ICU. This risk has to be minimized. We evaluate the performance of recommendation methods in suggesting appropriate medications and examine whether incorporating clinical patien...

Explainable Machine Learning with Two-Layer Multi-Objective Optimization Algorithm Applied to Sealing Structure Design.

This study addresses the challenge of optimizing seal structure design through a novel two-stage interpretable optimization framework. Focusing on O-r...

Assessing Healthcare Stakeholder Understanding of Machine Learning Documentation.

Artificial Intelligence (AI) has significantly advanced clinical decision support systems in healthcare, particularly using Machine Learning (ML) mode...

Federated Learning for Predictive Analytics in Weaning from Mechanical Ventilation.

Mechanical ventilation is crucial for critically ill patients in ICUs, requiring accurate weaning and extubations timing for optimal outcomes. Current...

Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios

This report introduces Aquarius, a family of industry-level video generation models for marketing scenarios designed for thousands-xPU clusters and ...

Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models

Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimens...

EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models

Recent advances in creative AI have enabled the synthesis of high-fidelity images and videos conditioned on language instructions. Building on these...

Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures

The rapid scaling of large language models (LLMs) has unveiled critical limitations in current hardware architectures, including constraints in memo...

Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms.

The choice of the optimal printing setup for Fused Deposition Modeling (FDM) 3D-printing technology is challenging due to complex interactions between...

Integrating Dynamic Red Blood Cell Distribution Width Monitoring and β-Blocker Therapy for Mortality Prediction in Intensive Care Unit Cardiomyopathy Patients: A Bayesian Multivariate Joint Model and Machine Learning Study.

Cardiomyopathy is a key cause of cardiovascular mortality in critically ill patients. Although red blood cell distribution width (RDW) is recognized ...

Prediction and Stage Classification of Pressure Ulcers in Intensive Care Patients by Machine Learning.

Pressure ulcers are a serious clinical problem associated with high morbidity, mortality and healthcare costs, especially in intensive care unit (ICU...

Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.

BACKGROUND: Early identification of children at risk of sepsis in emergency departments (EDs) is crucial for timely treatment and improved outcomes. E...

Predictors and associations of complications in ureteroscopy for stone disease using AI: outcomes from the FLEXOR registry.

We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid...

The Effect of Early Warning Systems for Sepsis on Mortality: A Systematic Review and Meta-analysis.

BACKGROUND: The Surviving Sepsis Campaign strongly recommends that all hospitals screen for sepsis as part of performance improvement. The effect of s...

An Innovative Deep Learning Approach for Ventilator-Associated Pneumonia (VAP) Prediction in Intensive Care Units-Pneumonia Risk Evaluation and Diagnostic Intelligence via Computational Technology (PREDICT).

Ventilator-associated pneumonia (VAP) is a common and serious ICU complication, affecting up to 40% of mechanically ventilated patients. The diagnosi...

Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.

Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increase...

Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective.

: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary...

Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by...

[Chest radiological lesions in COVID-19 : from classical imaging to artificial intelligence].

In the course of the pandemic induced by the appearance of a new coronavirus (SARS-CoV-2; COVID-19) causing acute respiratory distress syndrome (ARDS)...