AIMC Topic: Intensive Care Units, Neonatal

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Continuous wireless sensor monitoring with applied diagnostics: Clinical Sensor Pain Scale and Automated Sensor Pain Scale in the NICU.

BMJ health & care informatics
OBJECTIVES: Inappropriately treated pain can have deleterious outcomes in infants. Current tools rely on intermittent, subjective observation requiring specialised paediatric skills. This study aimed to diagnose infant pain through continuous monitor...

Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.

Journal of medical Internet research
BACKGROUND: The use of artificial intelligence (AI) in health care has been steadily increasing for over 2 decades. Integrating AI into neonatal intensive care units (NICUs) has promise as it has the potential to reshape neonatal care and improve out...

Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.

BMJ paediatrics open
BACKGROUND: Neonatal sepsis is a major cause of morbidity and mortality in low-resource settings and accurate, context-appropriate diagnostic methods are urgently needed to improve clinical outcomes.

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...

Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.

Scientific reports
Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining various statistical methods. This prediction could potentially reduce the negative health outcomes, d...

Intergenerational views on humanoid nurse robots in general wards, obstetrics and neonatal units.

Nursing ethics
BackgroundThis study explores intergenerational perspectives on the use of humanoid nurse robots in healthcare settings, recognizing the increasing relevance of robotic technologies and associated ethical, emotional, and privacy concerns.Research aim...

Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates.

Computers in biology and medicine
BACKGROUND: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine w...

Continuous non-contact monitoring of neonatal activity.

BMC pediatrics
PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is associated with under and over-sedation and may also indicate the onset of disease. Activity may also ca...

Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning.

Scientific reports
Echocardiography is the gold standard of diagnosis and evaluation of patent ductus arteriosus (PDA), a common condition among preterm infants that can cause hemodynamic abnormalities and increased mortality rates, but this technique requires a skille...

Artificial intelligence and informatics in neonatal resuscitation.

Seminars in perinatology
Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancemen...