AIMC Topic: Emergency Service, Hospital

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Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach.

BMJ open
OBJECTIVES: Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Gi...

Identification of neurological text markers associated with risk of stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Delayed or missed stroke diagnosis is associated with poor outcomes. We utilized natural language processing of notes from non-neurological emergency department (ED) encounters to identify text phrases indicating stroke presentations that...

Artificial Intelligence Solutions to Improve Emergency Department Wait Times: Living Systematic Review.

The Journal of emergency medicine
BACKGROUND: Overcrowding and long wait times in emergency departments (EDs) remain global challenges that negatively affect patient outcomes and staff satisfaction. As an emerging technology, artificial intelligence (AI) offers the potential to optim...

Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.

Journal of medical Internet research
BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to increased strain on health care systems. Efficient triage systems and accurate departmental guidance are...

Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.

Journal of substance use and addiction treatment
INTRODUCTION: Emergency departments (ED) are potential sites for identifying and treating individuals at high risk for opioid overdose. This study used machine learning (ML)-based models to predict opioid overdose death in the 12 months after an ED v...

Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.

Addiction (Abingdon, England)
AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...

The role of AI in emergency department triage: An integrative systematic review.

Intensive & critical care nursing
BACKGROUND: Overcrowding in emergency departments (EDs) leads to delayed treatments, poor patient outcomes, and increased staff workloads. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to optimize triage.

"I don't know": An uncertainty-aware machine learning model for predicting patient disposition at emergency department triage.

International journal of medical informatics
BACKGROUND: Machine learning (ML) models are widely used for predicting patient disposition at emergency department (ED) triage. However, these models generate predictions regardless of the level of uncertainty, potentially leading to overconfident o...

Utilizing machine learning for predicting mortality in patients with heat-related illness who visited the emergency department.

International journal of medical informatics
BACKGROUND: In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no predictio...

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

Computer methods and programs in biomedicine
BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medic...