Artificial intelligence universal biomarker prediction tool.
Yoshiyasu TakefujiPublished in: Journal of thrombosis and thrombolysis (2023)
Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and NP-proBNP for heart failure, to predict the next hospital visit. Predicting improvement in disease is a great hope for patients.
Keyphrases
- artificial intelligence
- end stage renal disease
- heart failure
- machine learning
- big data
- deep learning
- newly diagnosed
- chronic kidney disease
- type diabetes
- healthcare
- peritoneal dialysis
- emergency department
- prognostic factors
- metabolic syndrome
- atrial fibrillation
- cell cycle
- insulin resistance
- acute care
- acute heart failure
- cardiac resynchronization therapy