AI Medical Compendium Topic

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Emergency Service, Hospital

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Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Pediatric pulmonology
BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both envir...

Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

The journal of applied laboratory medicine
BACKGROUND: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study d...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

European heart journal. Acute cardiovascular care
AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning...

Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.

Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

Yonsei medical journal
PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within tim...

Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.

Hospital pediatrics
OBJECTIVE: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements fr...

[Not Available].

Recenti progressi in medicina
Our purpose was to evaluate the approach of two different chatbots (ChatGPT and Gemini) to a list of questions about emergency department as a border area between hospital and territory. This study was performed in a single day, on 3 March 2024. Two ...

Using Deep Learning to Suggest Treatment for Proximal Humerus Fractures.

Studies in health technology and informatics
Proximal humeral fractures are among the most common fractures seen in emergency departments. Accurately diagnosing and selecting the most appropriate treatment for these fractures can be challenging, and consultation with a senior orthopedic surgeon...

Proactive care management of AI-identified at-risk patients decreases preventable admissions.

The American journal of managed care
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).

Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (A...