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Length of Stay

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Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

PloS one
To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and Septem...

Using Artificial Intelligence to predict outcomes of operatively managed neck of femur fractures.

British journal of hospital medicine (London, England : 2005)
Patients with neck of femur fractures present a tremendous public health problem that leads to a high incidence of death and dysfunction. An essential factor is the postoperative length of stay, which heavily impacts hospital costs and the quality o...

Predicting hospital length of stay using machine learning on a large open health dataset.

BMC health services research
BACKGROUND: Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as health...

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

European journal of medical research
BACKGROUND: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolon...

A systematic literature review of predicting patient discharges using statistical methods and machine learning.

Health care management science
Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many hea...

Machine learning: implications and applications for ambulatory anesthesia.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.

BMC infectious diseases
BACKGROUND: Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cos...

Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor.

Studies in health technology and informatics
This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77....

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

Studies in health technology and informatics
This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient B...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Studies in health technology and informatics
Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem h...