Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone / Chicco, Davide; Jurman, Giuseppe. - In: BMC MEDICAL INFORMATICS AND DECISION MAKING. - ISSN 1472-6947. - ELETTRONICO. - 20:1(2020), p. 16. [10.1186/s12911-020-1023-5]

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Jurman, Giuseppe
2020-01-01

Abstract

Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records.
2020
1
Chicco, Davide; Jurman, Giuseppe
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone / Chicco, Davide; Jurman, Giuseppe. - In: BMC MEDICAL INFORMATICS AND DECISION MAKING. - ISSN 1472-6947. - ELETTRONICO. - 20:1(2020), p. 16. [10.1186/s12911-020-1023-5]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/343482
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