| |  Online-Ressource |
|---|
| Verfasst von: | Zhang, Yang [VerfasserIn]  |
|---|
| | Xiang, Tianyu [VerfasserIn] |
|---|
| | Wang, Yanqing [VerfasserIn] |
|---|
| | Shu, Tingting [VerfasserIn] |
|---|
| | Yin, Chengliang [VerfasserIn] |
|---|
| | Li, Huan [VerfasserIn] |
|---|
| | Duan, Minjie [VerfasserIn] |
|---|
| | Sun, Mengyan [VerfasserIn] |
|---|
| | Zhao, Binyi [VerfasserIn] |
|---|
| | Kadier, Kaisaierjiang [VerfasserIn] |
|---|
| | Xu, Qian [VerfasserIn] |
|---|
| | Ling, Tao [VerfasserIn] |
|---|
| | Kong, Fanqi [VerfasserIn] |
|---|
| | Liu, Xiaozhu [VerfasserIn] |
|---|
| Titel: | Explainable machine learning for predicting 30-day readmission in acute heart failure patients |
|---|
| Verf.angabe: | Yang Zhang, Tianyu Xiang, Yanqing Wang, Tingting Shu, Chengliang Yin, Huan Li, Minjie Duan, Mengyan Sun, Binyi Zhao, Kaisaierjiang Kadier, Qian Xu, Tao Ling, Fanqi Kong, Xiaozhu Liu |
|---|
| E-Jahr: | 2024 |
|---|
| Jahr: | 19 July 2024 |
|---|
| Umfang: | 12 S. |
|---|
| Illustrationen: | Illustrationen |
|---|
| Fussnoten: | Online verfügbar: 15. Juni 2024, Artikelversion: 27. Juni 2024 ; Gesehen am 17.03.2025 |
|---|
| Titel Quelle: | Enthalten in: iScience |
|---|
| Ort Quelle: | Amsterdam : Elsevier, 2018 |
|---|
| Jahr Quelle: | 2024 |
|---|
| Band/Heft Quelle: | 27(2024), 7, Artikel-ID 110281, Seite 1-12 |
|---|
| ISSN Quelle: | 2589-0042 |
|---|
| Abstract: | We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703-0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model. |
|---|
| DOI: | doi:10.1016/j.isci.2024.110281 |
|---|
| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1016/j.isci.2024.110281 |
|---|
| | kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S2589004224015062 |
|---|
| | DOI: https://doi.org/10.1016/j.isci.2024.110281 |
|---|
| Datenträger: | Online-Ressource |
|---|
| Sprache: | eng |
|---|
| Sach-SW: | bioinformatics |
|---|
| | cardiovascular medicine |
|---|
| K10plus-PPN: | 1919918000 |
|---|
| Verknüpfungen: | → Zeitschrift |
|---|
Explainable machine learning for predicting 30-day readmission in acute heart failure patients / Zhang, Yang [VerfasserIn]; 19 July 2024 (Online-Ressource)
