ArticleBMC medical informatics and decision making2019
Evidential MACE prediction of acute coronary syndrome using electronic health records.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Pooled it
- Machine Learning in Predicting Cardiac Events for ESRD Patients: A Framework for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Population-specific genetic differences of acute coronary syndrome in Han and Uyghur Chinese.Frontiers in pharmacology · 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
- SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
- Association between the triglyceride-glucose index and in-hospital major adverse cardiovascular events in patients with acute coronary syndrome: results from the Improving Care for Cardiovascular Disease in China (CCC)-Acute Coronary Syndrome project.Cardiovascular diabetology · 2024Observational
- Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.JMIR medical informatics · 2022Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Article
- Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.BMC medical informatics and decision making · 2020Article
- Prediction of blood culture outcome using hybrid neural network model based on electronic health records.BMC medical informatics and decision making · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals.
methodsTo remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. In details, four state-of-the-art models, including one traditional ACS risk scoring model, i.e., GRACE, and three machine learning based models, i.e., Support Vector Machine, L
resultsHaving applied the proposed method on a clinical dataset consisting of 2930 ACS patient samples, our model achieves 0.715 AUC value with competitive standard deviation, which is the best prediction results comparing with the four single base models and two baseline ensemble models.
conclusionsFacing with the limitations in traditional ACS risk scoring models, machine learning models and the uncertainties of EHR data, we present an ensemble approach via RST and DST to alleviate this problem. The experimental results reveal that our proposed method achieves better performance for the problem of MACE prediction when compared with the single models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.