Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.
Konrad PieszkoJarosław HiczkiewiczPaweł BudzianowskiJanusz RzeźniczakJan BudzianowskiJerzy BłaszczyńskiRoman SłowińskiPaweł BurchardtPublished in: Journal of translational medicine (2018)
Machine learned models can rely on the association between the elevated inflammatory markers and the short-term ACS outcomes to provide accurate predictions. Moreover, such models can help assess the usefulness of laboratory and clinical features in predicting the in-hospital mortality of ACS patients.
Keyphrases
- acute coronary syndrome
- end stage renal disease
- percutaneous coronary intervention
- ejection fraction
- antiplatelet therapy
- chronic kidney disease
- deep learning
- newly diagnosed
- oxidative stress
- prognostic factors
- peritoneal dialysis
- high resolution
- metabolic syndrome
- type diabetes
- adipose tissue
- insulin resistance
- patient reported
- neural network