AIMC Topic: Intensive Care Units

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BigLSTM: Recurrent neural network for the treatment of anomalous temporal signals. Application in the prediction of endotracheal obstruction in COVID-19 patients in the intensive care unit.

Computers in biology and medicine
Real-world applications, particularly in the medical field, often handle irregular time signals (ITS) with non-uniform intervals between measurements. These irregularities arise due to missing data, inconsistent sampling frequencies, and multi-sensor...

Evaluating the National Early Warning Score (NEWS) in triage: A machine learning perspective.

International emergency nursing
BACKGROUND: The National Early Warning Score is widely used in Emergency Departments for triage, primarily to predict mortality. However, its effectiveness in assessing additional clinical outcomes relevant to triage, such as patient urgency and seve...

The impact of AI-based decision support systems on nursing workflows in critical care units.

International nursing review
AIM: This research examines the effects of artificial intelligence (AI)-based decision support systems (DSS) on the operational processes of nurses in critical care units (CCU) located in Amman, Jordan.

Early efficacy observation of suspended lower-limb rehabilitation robot-assisted therapy in patients with intensive care unit-acquired weakness: a study protocol for a self-controlled randomised controlled trial.

BMJ open
INTRODUCTION: Intensive care unit-acquired weakness (ICUAW) is a common and severe complication in critically ill patients, associated with high morbidity and poor prognosis. Despite increasing focus on ICUAW, definitive diagnostic and therapeutic st...

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.

JMIR medical informatics
BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue time is short, and the effective way to reduce mortality is rapid diagnosis and early warning. Therefore, real-time prediction models play a key role i...

Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for tim...

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BMC medical informatics and decision making
BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine lea...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Nature communications
Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. Herein, blood samples from 586 in-house patients with suspected sepsis are used in conjunction with ...

ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

Critical care (London, England)
BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Existing prediction models for MAKE30 are static and overlook dynamic changes in clinical ...

Relationship between medication regimen complexity and pharmacist engagement in fluid stewardship.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
PURPOSE: The medication regimen complexity intensive care unit (MRC-ICU) score has previously been associated with pharmacist workload and fluid overload. The purpose of this study was to determine the relationship of MRC-ICU score with pharmacist-dr...