Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis.
Le LiZhuxin ZhangLikun ZhouZhenhao ZhangYulong XiongZhao HuYan YaoPublished in: European heart journal. Digital health (2023)
We established and validated a machine leaning-based prediction model, which was conducive to early identification of high-risk LTVA patients in sepsis, thus appropriate methods could be conducted to improve outcomes.
Keyphrases
- machine learning
- end stage renal disease
- acute kidney injury
- deep learning
- intensive care unit
- heart failure
- chronic kidney disease
- newly diagnosed
- ejection fraction
- septic shock
- prognostic factors
- peritoneal dialysis
- left ventricular
- metabolic syndrome
- catheter ablation
- big data
- atrial fibrillation
- bioinformatics analysis
- neural network