AIMC Topic: Cross Infection
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Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a health care setting.
American journal of infection control
Sep 21, 2024
BACKGROUND: Hospital-acquired infections (HAIs) increase morbidity, mortality, and health care costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning t...
Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.
Frontiers in public health
Sep 12, 2024
This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2,517 patients treated in the ICU of a tertiary hospital in Ch...
Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis.
Antimicrobial resistance and infection control
Aug 7, 2024
BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to develop and validate two machine learning models for NIs and in-hospital mortality risk prediction.
Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.
Antimicrobial resistance and infection control
Jul 6, 2024
BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO p...
Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.
BMC public health
Jul 4, 2024
BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) system...
Video-based automatic hand hygiene detection for operating rooms using 3D convolutional neural networks.
Journal of clinical monitoring and computing
Jun 19, 2024
Hand hygiene among anesthesia personnel is important to prevent hospital-acquired infections in operating rooms; however, an efficient monitoring system remains elusive. In this study, we leverage a deep learning approach based on operating room vide...
Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
May 21, 2024
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).
Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.
BMC infectious diseases
May 2, 2024
BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.
Two-step interpretable modeling of ICU-AIs.
Artificial intelligence in medicine
Mar 28, 2024
We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining the interpretability of the models. T...
Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance.
American journal of infection control
Mar 14, 2024
BACKGROUND: Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding s...
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