Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018.
Ziying LinYuen Ting ChengBernard Man Yung CheungPublished in: Annals of medicine (2023)
Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
Keyphrases
- machine learning
- artificial intelligence
- blood pressure
- hypertensive patients
- big data
- deep learning
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- peritoneal dialysis
- climate change
- patient reported outcomes
- randomized controlled trial
- clinical trial
- double blind