Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Background Up to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis. Hypothesis Machine learning (ML) methods can accurately identif...

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Veröffentlicht in:	Clinical cardiology (Mahwah, N.J.) N.J.), 2023-12, Vol.46 (12), p.1594-1602
Hauptverfasser:	Song, Long, Li, Yuan, Nie, Shanshan, Feng, Zeying, Liu, Yaxin, Ding, Fangfang, Gong, Liying, Liu, Liming, Yang, Guoping
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy acute coronary syndrome Acute Coronary Syndrome - diagnosis Acute Coronary Syndrome - therapy Acute coronary syndromes Acute Kidney Injury - diagnosis Acute Kidney Injury - etiology AKI Algorithms all‐cause mortality Angioplasty Artificial intelligence Blood platelets Blood pressure Cholesterol Clinical Trial Result Data processing Datasets Decision making Diabetes Discriminant analysis Hospitals Humans Hypertension Ischemia Kidney diseases Lymphocytes Machine Learning Mortality Myocardial Infarction Neutrophils Patients Percutaneous Coronary Intervention - adverse effects prognosis Retrospective Studies
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