AIMC Topic: Heart Arrest

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Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

Critical care medicine
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Annals of the American Thoracic Society
Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well-led IHCA resuscitation teams deliver better care, but expert resuscitation leaders are often unavai...

Outcome Prediction in Postanoxic Coma With Deep Learning.

Critical care medicine
OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve ...

Advancing In-Hospital Clinical Deterioration Prediction Models.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Early warning systems lack robust evidence that they improve patients' outcomes, possibly because of their limitation of predicting binary rather than time-to-event outcomes.

Visualizing patient journals by combining vital signs monitoring and natural language processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admi...

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Critical care medicine
OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter ...

The effects of sternal intraosseous and intravenous administration of amiodarone in a hypovolemic swine cardiac arrest model.

American journal of disaster medicine
OBJECTIVE: This study compared the effects of amiodarone via sternal intraosseous (SIO) and intravenous (IV) routes on return of spontaneous circulation (ROSC), time to ROSC, concentration maximum (C), time to maximum concentration (T), and mean conc...

The effects of tibial intraosseous versus intravenous amiodarone administration in a hypovolemic cardiac arrest procine model.

American journal of disaster medicine
OBJECTIVE: This study compared the effects of amiodarone via tibial intraosseous (TIO) and intravenous (IV) routes on return of spontaneous circulation (ROSC), time to ROSC, maximum drug concentration (Cmax), time to maximum concentration (Tmax), and...

The comparison of humeral intraosseous and intravenous administration of vasopressin on return of spontaneous circulation and pharmacokinetics in a hypovolemic cardiac arrest swine model.

American journal of disaster medicine
INTRODUCTION: The American Heart Association (AHA) recommends intravenous (IV) or intraosseous (IO) vasopressin in Advanced Cardiac Life Support (ACLS). Obtaining IV access in hypovolemic cardiac arrest patients can be difficult, and IO access is oft...