AI Medical Compendium Topic

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

Length of Stay

Showing 21 to 30 of 374 articles

Clear Filters

A Comparison of Endoscope-Assisted and Open Frontoorbital Distraction for the Treatment of Unicoronal Craniosynostosis.

Plastic and reconstructive surgery
BACKGROUND: Frontoorbital distraction osteogenesis (FODO) is an established surgical technique for patients with unicoronal craniosynostosis. The authors' institution has used an endoscope-assisted technique (endo-FODO) in recent years to decrease cu...

Clinical and socioeconomic predictors of hospital use and emergency department visits among children with medical complexity: A machine learning approach using administrative data.

PloS one
OBJECTIVES: The primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary object...

Interpretable machine learning for time-to-event prediction in medicine and healthcare.

Artificial intelligence in medicine
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challen...

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general populati...

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.

BMC medical informatics and decision making
BACKGROUND: Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the lengt...

Predicting stroke severity of patients using interpretable machine learning algorithms.

European journal of medical research
BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke se...

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Scientific reports
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...