Intensivists

Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assesse...

Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deterioration.

Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hospital adverse events. Standard episodic inpatient assessment of vital signs can potentially miss changes in health status and delay recognition of elevated risk. To reduce the likelihood of this delayed recognition of risk, we developed a wearable-based deep learning model, using only 9 inputs, to id...

Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.

OBJECTIVE: To explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an imp...

Hierarchical design of silkworm silk for functional composites.

Silk-reinforced composites (SRCs) manifest the unique properties of silkworm silk fibers, offering enhanced mechanical strength, biocompatibility, and...

Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.

BACKGROUND: Recent development in AI-driven predictive analytics have demonstrated potential to enhance critical care workflows, particularly in three...

Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis.

OBJECTIVE: The aim of our study was to establish and validate a machine learning-based predictive model for mortality risk in elderly patients with se...

Identification and Experimental Validation of Biomarkers Associated With Mitochondria and Macrophage Polarization in Sepsis.

Sepsis is a common and serious condition, where mitochondria and macrophage polarization play a crucial role. Therefore, this study aimed to identify...

Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.

As the aging population in the United States grows, the need for an integrated approach to support older adults has become increasingly urgent. The SU...

ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model.

Understanding protein functions facilitates the identification of the underlying causes of many diseases and guides the research for discovering new t...

Identification of Fatty Acid Metabolism Disorder-Related Gene Signature in Septic Cardiomyopathy.

BACKGROUND: Septic cardiomyopathy (SCM) is a prevalent complication of sepsis and a primary contributor to mortality in patients with sepsis. Although...

Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique.

: Sepsis leads to substantial global health burdens in terms of morbidity and mortality and is associated with numerous risk factors. It is crucial to...

Propofol-associated Hypertriglyceridemia: Development and Multicenter Validation of a Machine-Learning-Based Prediction Tool.

To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in c...

A Randomized, Blinded, Placebo-Controlled Crossover Study of the Pharmacokinetics and Pharmacodynamics of Naloxone, Naltrexone, and Nalmefene in Methadone-Sedated Working Dogs.

A randomized, blinded, placebo-controlled crossover study was performed with eight professional working dogs to evaluate the pharmacokinetics and phar...

Study on Spark Image Detection for Abrasive Belt Grinding via Transfer Learning with YOLOv8.

Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a mult...

Sculpting molecules in text-3D space: a flexible substructure aware framework for text-oriented molecular optimization.

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promi...

Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit.

BACKGROUND: The incidence of ventilator-associated pneumonia (VAP) in ischemic stroke (IS) patients is linked to a variety of detrimental outcomes. Cu...

Analysis of collapse risks under cut and cover method based on multi-state fuzzy Bayesian network.

The collapse accidents under cut and cover method in metro station construction occurred frequently, leading to severe casualties and property damage....

Machine learning-based text mining for cutaneous myiasis and potential value of an accidental maggot therapy for complicated skin and soft tissue infection with sepsis.

BACKGROUND: Cutaneous myiasis, one of the most frequently diagnosed myiasis types, is defined as skin or soft tissue on a living host infested by dipt...

Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.

OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocomprom...

ADT²R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis.

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in t...