{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Park JI"],"funding":["NCATS NIH HHS"],"pagination":["278-287"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8568050"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["53(3)"],"pubmed_abstract":["<h4>Purpose</h4>The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).<h4>Design</h4>This study was a retrospective, observational study.<h4>Methods</h4>We extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital. Then we developed and evaluated three prediction models using logistic regression, the balanced random forest model, and the multilayer perceptron.<h4>Results</h4>The study sample included 158,804 total admissions; VTE-positive cases accounted for 2,080 admissions from among 1,695 patients (1.31%). Based on the evaluation results, the balanced random forest model outperformed the other two risk prediction models.<h4>Conclusions</h4>This study delivered a high-performing, validated risk prediction tool using machine learning and EHRs to identify patients at high risk for VTE after discharge.<h4>Clinical relevance</h4>The risk prediction model developed in this study can potentially guide treatment decisions for discharged patients for better patient outcomes."],"journal":["Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing"],"pubmed_title":["Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning."],"pmcid":["PMC8568050"],"funding_grant_id":["UL1 TR001414"],"pubmed_authors":["Zheng K","Kim D","Park JI","Lee JA","Amin A"],"additional_accession":[]},"is_claimable":false,"name":"Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.","description":"<h4>Purpose</h4>The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).<h4>Design</h4>This study was a retrospective, observational study.<h4>Methods</h4>We extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital. Then we developed and evaluated three prediction models using logistic regression, the balanced random forest model, and the multilayer perceptron.<h4>Results</h4>The study sample included 158,804 total admissions; VTE-positive cases accounted for 2,080 admissions from among 1,695 patients (1.31%). Based on the evaluation results, the balanced random forest model outperformed the other two risk prediction models.<h4>Conclusions</h4>This study delivered a high-performing, validated risk prediction tool using machine learning and EHRs to identify patients at high risk for VTE after discharge.<h4>Clinical relevance</h4>The risk prediction model developed in this study can potentially guide treatment decisions for discharged patients for better patient outcomes.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 May","modification":"2025-04-04T19:57:54.236Z","creation":"2025-04-04T19:57:54.236Z"},"accession":"S-EPMC8568050","cross_references":{"pubmed":["33617689"],"doi":["10.1111/jnu.12637"]}}