Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing

dc.contributor.authorOrangi-Fard, Negar
dc.contributor.authorAkhbardeh, Alireza
dc.contributor.authorSagreiya, Hersh
dc.contributor.departmentGeorgia Gwinnett College; The School of Medicine, Johns Hopkins University; Perelman School of Medicine, University of Pennsylvaniaen_US
dc.date.accessioned2022-02-02T18:53:02Z
dc.date.available2022-02-02T18:53:02Z
dc.date.issued2022-01-26
dc.descriptionAMA Style Orangi-Fard N, Akhbardeh A, Sagreiya H. Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing. Informatics. 2022; 9(1):10. https://doi.org/10.3390/informatics9010010 Chicago/Turabian Style Orangi-Fard, Negar, Alireza Akhbardeh, and Hersh Sagreiya. 2022. "Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing" Informatics 9, no. 1: 10. https://doi.org/10.3390/informatics9010010en_US
dc.descriptionAMA Style Orangi-Fard N, Akhbardeh A, Sagreiya H. Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing. Informatics. 2022; 9(1):10. https://doi.org/10.3390/informatics9010010 Chicago/Turabian Style Orangi-Fard, Negar, Alireza Akhbardeh, and Hersh Sagreiya. 2022. "Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing" Informatics 9, no. 1: 10. https://doi.org/10.3390/informatics9010010en_US
dc.description.abstractPredicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III).We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well.en_US
dc.identifier.citationOrangi-Fard, N.; Akhbardeh, A.; Sagreiya, H. Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing. Informatics 2022, 9, 10. https://doi.org/10.3390/informatics9010010en_US
dc.identifier.issn2227-9709
dc.identifier.journalInformaticsen_US
dc.identifier.urihttps://generalspace.ggc.edu/handle/10675.3/610858
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.relation.urlhttps://doi.org/10.3390/informatics9010010en_US
dc.subjectnatural language processingen_US
dc.subjectmachine learningen_US
dc.subjectintensive care uniten_US
dc.subjectreadmissionen_US
dc.subjecthealth informaticeen_US
dc.titlePredictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processingen_US
dc.typeArticleen_US
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