Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

CONCLUSIONS: The use of extensive phenotype data and novel machine learning improves prediction of mortality in ACS over traditional GRACE score. KEY MESSAGES The collection of extensive cardiovascular phenotype data from electronic health records as well as from data recorded by physicians can be used highly effectively in prediction of mortality after acute coronary syndrome. Supervised machine learning methods such as logistic regression and extreme gradient boosting using extensive phenotype data significantly outperform conventional risk assessment by the current golden standard GRACE score. Integration of electronic health records and the use of supervised machine learning methods can be easily applied in a single centre level to model the risk of mortality. PMID: 31030570 [PubMed - as supplied by publisher]
Source: Annals of Medicine - Category: Internal Medicine Tags: Ann Med Source Type: research