ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Integrating phenotypic information of obstructive sleep apnea and deep representation of sleep-event sequences for cardiovascular risk prediction.BMC medical informatics and decision making · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- Machine learning prediction of mortality in Acute Myocardial Infarction.BMC medical informatics and decision making · 2023Article
- Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.Scientific reports · 2023Article
- Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.Journal of personalized medicine · 2022Article
- Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients.BMC medical informatics and decision making · 2022Article
- Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction.BMC medical informatics and decision making · 2021Article
- Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.Scientific reports · 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
- Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data.BMC medical informatics and decision making · 2020Article
- Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.BMC medical informatics and decision making · 2020Article
- DeepConsensus: Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction.Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks · 2020Article
- Using Machine Learning to Predict Hyperchloremia in Critically Ill Patients.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.Studies in health technology and informatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Heart disease remains the leading cause of death in the United States. Compared with risk assessment guidelines that require manual calculation of scores, machine learning-based prediction for disease outcomes such as mortality can be utilized to save time and improve prediction accuracy. This study built and evaluated various machine learning models to predict one-year mortality in patients diagnosed with acute myocardial infarction or post myocardial infarction syndrome in the MIMIC-III database. The results of the best performing shallow prediction models were compared to a deep feedforward neural network (Deep FNN) with back propagation. We included a cohort of 5436 admissions. Six datasets were developed and compared. The models applying Logistic Model Trees (LMT) and Simple Logistic algorithms to the combined dataset resulted in the highest prediction accuracy at 85.12% and the highest AUC at .901. In addition, other factors were observed to have an impact on outcomes as well.
Identifiers
31258994PMC6568079What 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.