AIMC Topic: Emergency Service, Hospital
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Human-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI.
International journal of medical informatics
Jan 28, 2025
BACKGROUND: Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data...
A pediatric emergency prediction model using natural language process in the pediatric emergency department.
Scientific reports
Jan 28, 2025
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021...
Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK.
PloS one
Jan 27, 2025
During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan t...
AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
Journal of medical Internet research
Jan 24, 2025
BACKGROUND: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection...
Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.
JMIR medical informatics
Jan 21, 2025
BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured ...
Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Jan 17, 2025
OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. However, there are significant errors in the communication and follow-up of incidental findings on E...
Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.
International journal of molecular sciences
Jan 16, 2025
The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning ...
Predicting delayed neurological sequelae in patients with carbon monoxide poisoning using machine learning models.
Clinical toxicology (Philadelphia, Pa.)
Jan 14, 2025
INTRODUCTION: Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life of patients with the condition. We aimed to develop a machine learning-based prediction model to...
Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels.
Pediatric emergency care
Jan 7, 2025
OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric eme...
Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
BMC emergency medicine
Jan 6, 2025
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
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