ArticleJournal of translational medicine2018
Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.
Article in Journal of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 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
- Pooled it
- Machine learning and traditional regression approaches to predict risk of cardiovascular events in patients with acute coronary syndrome.BMC medical informatics and decision making · 2026Observational
- Effect of pesticide exposure on systemic inflammatory biomarkers: a meta-analysis, and trial sequential analysis.Journal of environmental health science & engineering · 2025Review
- HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis.Scientific reports · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry.Journal of medical Internet research · 2024Article
- Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.PloS one · 2024Article
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores.European heart journal. Digital health · 2023Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Confounders in Identification and Analysis of Inflammatory Biomarkers in Cardiovascular Diseases.Biomolecules · 2021Review
- Clinical applications of artificial intelligence in cardiology on the verge of the decade.Cardiology journal · 2021Review
- Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation.ESC heart failure · 2020Article
- Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.Disease markers · 2019Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIncreased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in predicting short-term outcomes of acute coronary syndrome (ACS).
methodsWe analyzed the predictive importance of laboratory and clinical features in 6769 hospitalizations of patients with ACS. Two binary classifications were considered: significant coronary lesion (SCL) or lack of SCL, and in-hospital death or survival. SCL was observed in 73% of patients. In-hospital mortality was observed in 1.4% of patients and it was higher in the case of patients with SCL. Ensembles of decision trees and decision rule models were trained to predict these classifications.
resultsThe best performing model for in-hospital mortality was based on the dominance-based rough set approach and the full set of laboratory as well as clinical features. This model achieved 81 ± 2.4% sensitivity and 81.1 ± 0.5% specificity in the detection of in-hospital mortality. The models trained for SCL performed considerably worse. The best performing model for detecting SCL achieved 56.9 ± 0.2% sensitivity and 66.9 ± 0.2% specificity. Dominance rough set approach classifier operating on the full set of clinical and laboratory features identifies presence or absence of diabetes, systolic and diastolic blood pressure and prothrombin time as having the highest confirmation measures (best predictive value) in the detection of in-hospital mortality. When we used the limited set of variables, neutrophil count, age, systolic and diastolic pressure and heart rate (taken at admission) achieved the high feature importance scores (provided by the gradient boosted trees classifier) as well as the positive confirmation measures (provided by the dominance-based rough set approach classifier).
conclusionsMachine 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.
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