AI Medical Compendium Topic

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Intensive Care Units

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Artificial intelligence to advance acute and intensive care medicine.

Current opinion in critical care
PURPOSE OF REVIEW: This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities...

Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data.

Journal of clinical monitoring and computing
Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops...

Determining steady-state trough range in vancomycin drug dosing using machine learning.

Journal of critical care
BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate d...

An automated ICU agitation monitoring system for video streaming using deep learning classification.

BMC medical informatics and decision making
OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

European journal of internal medicine
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA...

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals.

Scientific reports
The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objective...

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Scientific reports
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools ar...

Artificial intelligence to predict bed bath time in Intensive Care Units.

Revista brasileira de enfermagem
OBJECTIVES: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.

Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol.

Intensive & critical care nursing
BACKGROUND: Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing their coping and understanding of their experiences. Staff shortages and a limited amount of time severely restrict the application of ICU diaries. To...

INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.

Shock (Augusta, Ga.)
The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for...