AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Triage

Showing 51 to 60 of 270 articles

Clear Filters

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta.

International journal of molecular sciences
Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matri...

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

International journal of mental health nursing
This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T...

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

Scientific reports
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, ...

Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

Surgical innovation
BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use mac...

[Not Available].

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

International journal of medical informatics
INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic e...

Development and validation of a machine learning framework for improved resource allocation in the emergency department.

The American journal of emergency medicine
OBJECTIVE: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtr...

Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.

BMC primary care
BACKGROUND: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation ...

Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.

Computers in biology and medicine
BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as...