AIMC Topic: Intensive Care Units

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Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

BMC medical informatics and decision making
BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

International journal of cardiology
BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive care units (ICUs). Effective risk assessment tools are essential to improve patient outcomes. This study aims to evaluate the association between Simpl...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BMC medical informatics and decision making
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial ...

Clinicians' Perceptions and Potential Applications of Robotics for Task Automation in Critical Care: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of roboti...

Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: Analysis using the MIMIC-IV database.

Computers in biology and medicine
OBJECTIVE: This study aims to construct and compare four machine learning models using the MIMIC-IV database to identify high-risk factors for multidrug-resistant organism (MDRO) infection in Ventilator-associated pneumonia (VAP) patients.

Constructing an early warning model for elderly sepsis patients based on machine learning.

Scientific reports
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machin...

Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Acute Kidney Injury (AKI) is the sudden onset of kidney damage. This damage usually comes without warning and can lead to increased mortality and inpatient costs and is of particular significance to patients undergoing cancer treatment. In p...

Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.

Critical care explorations
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not t...

Point-of-Care Sensors and Medical Internet-of-Things Technologies to Manage Catheter-Associated Urinary Tract Infections in the Intensive Care Unit.

Critical care nursing clinics of North America
This article examines the current technologies used with indwelling urinary catheters to monitor potential catheter-associated urinary tract infections (CAUTIs) in the intensive care unit. Advancements in medical internet-of-things, artificial intell...

The future of artificial intelligence in cardiovascular monitoring.

Current opinion in critical care
PURPOSE OF REVIEW: Cardiovascular monitoring is essential for managing hemodynamic instability and preventing complications in critically ill patients. Conventional monitoring approaches are limited by predefined thresholds, dependence on clinician e...