AIMC Topic: Emergency Service, Hospital
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Interpretable machine learning for predicting sepsis risk in emergency triage patients.
Scientific reports
Jan 6, 2025
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
European radiology
Dec 31, 2024
OBJECTIVES: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by ra...
Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.
Journal of biomedical informatics
Dec 31, 2024
OBJECTIVE: Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process requires the simultaneous pursuit of multiple resuscitation goals. Recognizing whether the required g...
Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.
Journal of medical Internet research
Dec 31, 2024
BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage o...
A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.
PloS one
Dec 27, 2024
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...
Machine-learning based prediction of appendicitis for patients presenting with acute abdominal pain at the emergency department.
World journal of emergency surgery : WJES
Dec 23, 2024
BACKGROUND: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with th...
Evaluating LLM-based generative AI tools in emergency triage: A comparative study of ChatGPT Plus, Copilot Pro, and triage nurses.
The American journal of emergency medicine
Dec 19, 2024
BACKGROUND: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accur...
Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...
Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.
BMC emergency medicine
Dec 18, 2024
BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study de...
SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning...
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