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Intensive Care, Neonatal

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Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.

Advances in experimental medicine and biology
UNLABELLED: In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and...

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

The Lancet. Child & adolescent health
BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could ...

Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive careĀ setting.

Pharmacology research & perspectives
Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model...

Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques.

The Journal of perinatal & neonatal nursing
BACKGROUND: One of the scales most frequently used in the evaluation of newborn comfort levels is the Neonatal Comfort Behavior Scale (NCBS). It is important therefore that an increased use of the NCBS is encouraged through a more practical method of...

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...