An interpretable predictive model can increase transparency and help doctors accurately predict the occurrence of MCE in LHI patients, not undergoing recanalization therapy within 48h from onset, providing patients with better treatment strategies and enabling optimal resource allocation.
Keyphrases
- artificial intelligence
- end stage renal disease
- machine learning
- big data
- newly diagnosed
- ejection fraction
- chronic kidney disease
- deep learning
- risk assessment
- prognostic factors
- peritoneal dialysis
- stem cells
- subarachnoid hemorrhage
- middle cerebral artery
- mesenchymal stem cells
- patient reported outcomes
- replacement therapy