Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.
Chih-Wei SungJoshua HoCheng-Yi FanChing-Yu ChenChi-Hsin ChenShao-Yung LinJia-How ChangJiun-Wei ChenEdward Pei-Chuan HuangPublished in: BMJ health & care informatics (2024)
The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model.
Keyphrases
- machine learning
- heart rate
- emergency department
- blood pressure
- heart rate variability
- deep learning
- artificial intelligence
- hypertensive patients
- big data
- physical activity
- heart failure
- left ventricular
- blood glucose
- convolutional neural network
- neural network
- community dwelling
- middle aged
- metabolic syndrome
- case control
- weight loss