AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Hospitalization

Showing 401 to 410 of 463 articles

Clear Filters

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

Studies in health technology and informatics
Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical mar...

Using Artificial Intelligence for the Early Detection of Micro-Progression of Pressure Injuries in Hospitalized Patients: A Preliminary Nursing Perspective Evaluation.

Studies in health technology and informatics
This study established a predictive model for the early detection of micro-progression of pressure injuries (PIs) from the perspective of nurses. An easy and programing-free artificial intelligence modeling tool with professional evaluation capabilit...

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

Studies in health technology and informatics
OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.

Parkland Trauma Index of Mortality: Real-Time Predictive Model for Trauma Patients.

Journal of orthopaedic trauma
OBJECTIVE: Vital signs and laboratory values are used to guide decisions to use damage control techniques in lieu of early definitive fracture fixation. Previous models attempted to predict mortality risk but have limited utility. There is a need for...

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

Studies in health technology and informatics
Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We devel...

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology.

Mathematical biosciences and engineering : MBE
Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making is...

Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study.

The Lancet. Digital health
BACKGROUND: Patients have an estimated mortality of 15-20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing t...

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Pediatric emergency care
BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we develo...

[Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective admissions].

Revue medicale suisse
Efficient management of hospitalized patients requires carefully planning each stay by taking into account patients' pathologies and hospital constraints. Therefore, the ability to accurately estimate length of stays allows for better interprofession...