ArticleBMC medical informatics and decision making2020
A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 33 citations in OpenAlex.
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- 1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.Medicina (Kaunas, Lithuania) · 2024Article
- Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease.Frontiers in cardiovascular medicine · 2024Article
- Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions.Risk management and healthcare policy · 2024Review
- Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.BMC medical informatics and decision making · 2023Article
- Machine learning prediction of mortality in Acute Myocardial Infarction.BMC medical informatics and decision making · 2023Article
- Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.Frontiers in cardiovascular medicine · 2023Article
- Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction.Journal of personalized medicine · 2022Article
- Intelligent Identification and Features Attribution of Saline-Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy.Plants (Basel, Switzerland) · 2022Article
- Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction.Journal of cardiovascular development and disease · 2022Article
- Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics.BioMed research international · 2022Article
- Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.BMC medical informatics and decision making · 2021Article
- Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels.Journal of translational medicine · 2021Article
- Thrombus management during direct coronary intervention for acute myocardial infarction.American journal of translational research · 2021Article
- Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAcute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources.
methodsIn this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.
resultsThe results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713).
conclusionIt is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
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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.