ArticleDisease markers2019
Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.
Article in Disease markers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis.European journal of medical research · 2023Pooled it
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- Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction.BMC cardiovascular disorders · 2025Article
- Predicting in-hospital mortality in ICU patients with Coronary heart disease and diabetes mellitus using machine learning models.PloS one · 2025Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
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- Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements.International journal of legal medicine · 2023Article
- A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022.Frontiers in cardiovascular medicine · 2023Article
- The Clinical Value of Systemic Inflammatory Response Index and Inflammatory Prognosis Index in Predicting 3-Month Outcome in Acute Ischemic Stroke Patients with Intravenous Thrombolysis.International journal of general medicine · 2022Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Clinical impact of estimated plasma volume status and its additive effect with the GRACE risk score on in-hospital and long-term mortality for acute myocardial infarction.International journal of cardiology. Heart & vasculature · 2021Article
- Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality.ESC heart failure · 2021Article
- Clinical applications of artificial intelligence in cardiology on the verge of the decade.Cardiology journal · 2021Review
- Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image.Disease markers · 2021Article
- Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation.ESC heart failure · 2020Article
- Evaluation of a novel score for predicting 2-year outcomes in patients with acute coronary syndrome after percutaneous coronary intervention.Journal of the Chinese Medical Association : JCMA · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before.
objectiveWe aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers. PATIENTS AND
methodsWe obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period. The time of follow-up ranged from 12 to 72 months. A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission. Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset.
resultsFor in-hospital mortality, our model achieved a
conclusionsHematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome. A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.
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