ArticleStudies in health technology and informatics2019
Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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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.
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Who cites it
11 citing papers in PubMed.
- Incremental prognostic value of pan-immune-inflammation value in conservatively treated acute myocardial infarction: derivation and MIMIC-IV validation.Frontiers in cardiovascular medicine · 2026Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification.Frontiers in artificial intelligence · 2025Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- Machine learning prediction of mortality in Acute Myocardial Infarction.BMC medical informatics and decision making · 2023Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction.BMC medical informatics and decision making · 2021Article
- Establishment of a prognostic model based on the Sequential Organ Failure Assessment score for patients with first-time acute myocardial infarction.The Journal of international medical research · 2021Article
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
- Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.BMC medical informatics and decision making · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care. In our previous work, we built computational models to predict one-year mortality of patients admitted to an intensive care unit (ICU) with AMI or post myocardial infarction syndrome. Our prior work only used the structured clinical data from MIMIC-III, a publicly available ICU clinical database. In this study, we enhanced our work by adding the word embedding features from free-text discharge summaries. Using a richer set of features resulted in significant improvement in the performance of our deep learning models. The average accuracy of our deep learning models was 92.89% and the average F-measure was 0.928. We further reported the impact of different combinations of features extracted from structured and/or unstructured data on the performance of the deep learning models.
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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.