AI Medical Compendium Topic

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Emergency Service, Hospital

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Can artificial intelligence help ED nurses more accurately triage patients?

Nursing
The Emergency Severity Index (ESI) is the most popular tool used to triage patients in the US and abroad. Evidence has shown that ESI has its limitations in correctly assigning acuity. To address this, AI can be incorporated into the triage process, ...

Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

ESC heart failure
AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared wit...

Can Artificial Intelligence Be Utilized to Predict Real-Time Adverse Outcomes in Individuals Arriving at the Emergency Department With Hyperglycemic Crises?: Implications for APRN Practice.

Advanced emergency nursing journal
This column on translating research into practice is crafted to offer advanced practice registered nurses an analysis of current research topics that hold practical relevance for emergency care settings. The article titled "Using Artificial Intellige...

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score.

BMC medical informatics and decision making
BACKGROUND: Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for pred...

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

Computers, informatics, nursing : CIN
As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arr...

Emergency department risk model: timely identification of patients for outpatient care coordination.

The American journal of managed care
OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patie...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Scientific reports
Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled ...

An ensemble model for predicting dispositions of emergency department patients.

BMC medical informatics and decision making
OBJECTIVE: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED disposit...

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.

BMJ health & care informatics
BACKGROUND: High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has...