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dc.contributor.authorArenson, Michael
dc.contributor.authorHogan, Julien
dc.contributor.authorXu, Liyan
dc.contributor.authorLynch, Raymond
dc.contributor.authorLee, Yi-Ting Hana
dc.contributor.authorChoi, Jinho D
dc.contributor.authorSun, Jimeng
dc.contributor.authorAdams, Andrew
dc.contributor.authorPatzer, Rachel E
dc.date.accessioned2024-04-26T19:54:21Z
dc.date.available2024-04-26T19:54:21Z
dc.date.issued2022-12-12
dc.identifier.citationArenson M, Hogan J, Xu L, Lynch R, Lee YH, Choi JD, Sun J, Adams A, Patzer RE. Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data. Kidney Int Rep. 2022 Dec 12;8(3):489-498. doi: 10.1016/j.ekir.2022.12.006. PMID: 36938078; PMCID: PMC10014371.en_US
dc.identifier.eissn2468-0249
dc.identifier.doi10.1016/j.ekir.2022.12.006en_US
dc.identifier.pmid36938078
dc.identifier.urihttp://hdl.handle.net/20.500.14038/53308
dc.description.abstractIntroduction: Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization. Methods: In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005-2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance. Results: Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105). Conclusions: Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.en_US
dc.language.isoenen_US
dc.relation.ispartofKidney International Reportsen_US
dc.relation.urlhttps://doi.org/10.1016/j.ekir.2022.12.006en_US
dc.rightsCopyright 2022 Published by Elsevier, Inc., on behalf of the International Society of Nephrology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectearly readmissionen_US
dc.subjectkidney transplantationen_US
dc.subjectmachine learningen_US
dc.subjectnatural language processingen_US
dc.subjectpredicting readmissionen_US
dc.subjectrisk predictionen_US
dc.titlePredicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Dataen_US
dc.typeJournal Articleen_US
dc.source.journaltitleKidney international reports
dc.source.volume8
dc.source.issue3
dc.source.beginpage489
dc.source.endpage498
dc.source.countryUnited States
dc.identifier.journalKidney international reports
refterms.dateFOA2024-04-26T19:54:22Z
dc.contributor.departmentPediatricsen_US


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Copyright 2022 Published by Elsevier, Inc., on behalf of the International Society of Nephrology. This is an open access article
under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Except where otherwise noted, this item's license is described as Copyright 2022 Published by Elsevier, Inc., on behalf of the International Society of Nephrology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).