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
- machine learning
- heart failure
- big data
- ejection fraction
- newly diagnosed
- deep learning
- chronic kidney disease
- healthcare
- type diabetes
- cardiovascular disease
- prognostic factors
- peritoneal dialysis
- emergency department
- patient reported outcomes
- left ventricular
- metabolic syndrome
- atrial fibrillation
- insulin resistance
- adverse drug