AIMC Topic: Emergency Medical Services

Clear Filters Showing 1 to 10 of 93 articles

Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.

Scientific reports
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...

Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting.

Emergency medicine journal : EMJ
BACKGROUND: Early intervention in patients with major traumatic injuries is critical. Decision support can improve clinicians' ability to identify high-risk patients. The aim of this study was to compare the performance of a machine-learning (ML) dec...

Energy-efficient communication between IoMT devices and emergency vehicles for improved patient care.

PloS one
The rising integration of emergency healthcare services with the Internet of Medical Things (IoMT) creates a significant opportunity to improve real-time communication between patients and emergency vehicles like ambulances. Fast and reliable data in...

Emergency medical services providers' perspectives on the use of artificial intelligence in prehospital identification of stroke- a qualitative study in Norway and Sweden.

BMC emergency medicine
BACKGROUND: Stroke is a large and increasing health challenge, leading to acquired physical disability and mortality. A rapid diagnostic assessment in the acute phase of a stroke is crucial and highly time dependent. Studies suggest that artificial i...

Evolving role of point-of-care ultrasound in prehospital emergency care: a narrative review.

Scandinavian journal of trauma, resuscitation and emergency medicine
Point-of-care ultrasound is an emerging technology in prehospital emergency care, covering a wide range of medical and traumatic disease patterns. As an ad-hoc imaging modality, it is performed on-scene and during ground or aeromedical transport, ena...

Machine learning to improve predictive performance of prehospital early warning scores.

Scientific reports
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...

Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.

BMJ health & care informatics
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the EC...

Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.

BMC medical informatics and decision making
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...

A Machine Learning Trauma Triage Model for Critical Care Transport.

JAMA network open
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...