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Length of Stay

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Using AI to Predict Patients' Length of Stay: PACU Staff's Needs and Expectations for Developing and Implementing an AI System.

Journal of nursing management
The need for innovative technology in healthcare is apparent due to challenges posed by the lack of resources. This study investigates the adoption of AI-based systems, specifically within the postanesthesia care unit (PACU). The aim of the study wa...

Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.

PloS one
OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.

Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

Biometrics
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatm...

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.

BMC health services research
BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefo...

Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

Journal of healthcare management / American College of Healthcare Executives
GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than 50,000 discharges and 30,000 surgical procedures per year. TGH collaborated with GE HealthCare Comm...

Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

Artificial intelligence in medicine
In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, o...

Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.

BMC nephrology
BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks ...

Predicting prolonged length of in-hospital stay in patients with non-ST elevation myocardial infarction (NSTEMI) using artificial intelligence.

International journal of cardiology
BACKGROUND: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are typically evaluated using coronary angiography and managed through coronary revascularization. Numerous studies have demonstrated the benefits of expedited disch...