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
- chronic kidney disease
- ejection fraction
- newly diagnosed
- deep learning
- type diabetes
- healthcare
- peritoneal dialysis
- cardiovascular disease
- prognostic factors
- emergency department
- left ventricular
- patient reported outcomes
- skeletal muscle