Intensivists

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.

BACKGROUND: Agitation and sedation management is critical in intensive care as it affects patient safety. Traditional nursing assessments suffer from low frequency and subjectivity. Automating these assessments can boost intensive care unit (ICU) efficiency, treatment capacity, and patient safety.

Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database.

OBJECTIVE: This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.

Prioritisation of functional needs for ICU intelligent robots in China: a consensus study based on the national survey and nominal group technique.

OBJECTIVE: This study aims to define the prioritisation of the needs for an intelligent robot's functions in the intensive care unit (ICU) from a clin...

Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors.

The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The con...

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is asso...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mort...

Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture-Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification.

Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and impro...

Exploring hyperelastic material model discovery for human brain cortex: Multivariate analysis vs. artificial neural network approaches.

The human brain, characterized by its intricate architecture, exhibits complex mechanical properties that underpin its critical functional capabilitie...

Resilience of hierarchical actuators highlighted by a myosin-to-muscle mock-up.

Skeletal muscle is the main actuator of various families of vertebrates (mammals, fish, reptiles). It displays remarkable robustness to micro-damage, ...

Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine le...

Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care.

Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether t...

Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.

BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of...

User-Oriented Requirements for Artificial Intelligence-Based Clinical Decision Support Systems in Sepsis: Protocol for a Multimethod Research Project.

BACKGROUND: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the...

Design and mechanical analysis of a novel modular bionic earthworm robot with upright functionality.

With the escalating demand for exploration within confined spaces, bionic design methodologies have attracted considerable attention from researchers,...

Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRR...

Multi-view learning with enhanced multi-weight vector projection support vector machine.

Multi-view learning aims on learning from the data represented by multiple distinct feature sets. Various multi-view support vector machine methods ha...

Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.

OBJECTIVES: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening an...

Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol.

INTRODUCTION: Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite i...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting millions, costing billions, and claiming nearly 2 mil...