Development and validation of machine learning models to predict the need for haemostatic therapy in acute upper gastrointestinal bleeding.
Scarlet NazarianFrank Po Wen LoJianing QiuNisha PatelBenny LoLakshmana AyaruPublished in: Therapeutic advances in gastrointestinal endoscopy (2024)
We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy.
Keyphrases
- machine learning
- end stage renal disease
- artificial intelligence
- ejection fraction
- newly diagnosed
- deep learning
- chronic kidney disease
- big data
- liver failure
- stem cells
- peritoneal dialysis
- prognostic factors
- respiratory failure
- patient reported outcomes
- mesenchymal stem cells
- aortic dissection
- extracorporeal membrane oxygenation
- breast cancer risk