Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

Chin-Chuan HsuYuan KaoChien-Chin HsuChia-Jung ChenShu-Lien HsuTzu-Lan LiuHung-Jung LinJhi-Joung WangChung-Feng LiuChien-Cheng Huang

Published in: BMC endocrine disorders (2023)

A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Keyphrases

artificial intelligence
emergency department
big data
machine learning
end stage renal disease
deep learning
ejection fraction
newly diagnosed
chronic kidney disease
prognostic factors
peritoneal dialysis
patient reported outcomes
patient reported
adverse drug