دورية أكاديمية
Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.
| العنوان: | Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit. |
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| المؤلفون: | Mao B; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China., Ling L; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China., Pan Y; Urology Department, Lin'an Hospital of Traditional Chinese Medicine, Hangzhou, 311321, China., Zhang R; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China.; The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, China., Zheng W; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China.; The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, China., Shen Y; Department of Intensive Care, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, 310030, China., Lu W; ArteryFlow Technology Co., Ltd., Hangzhou, 310051, China., Lu Y; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China.; The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, China., Xu S; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China., Wu J; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China., Wang M; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China. minghui0507@zju.edu.cn., Wan S; Brain center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, 1229 Gudun Road, Hangzhou, 310030, China. wanshu@zju.edu.cn. |
| المصدر: | Scientific reports [Sci Rep] 2024 Jun 20; Vol. 14 (1), pp. 14195. Date of Electronic Publication: 2024 Jun 20. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مواضيع طبية MeSH: | Machine Learning* , Hospital Mortality* , Cerebral Hemorrhage*/mortality , Intensive Care Units*, Humans ; Male ; Female ; Middle Aged ; Aged ; Retrospective Studies ; Prognosis |
| مستخلص: | This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems. (© 2024. The Author(s).) |
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| معلومات مُعتمدة: | LY21H090008 Natural Science Foundation of Zhejiang Province; WKJ-ZJ-2340 Medical Health Science and Technology Project of Zhejiang Provincial Health Commission; 2022R52038 High Level Talents of Zhejiang Province; 2021C03105 Key Research and Development Project of Zhejiang Provincial Department of Science and Technology |
| فهرسة مساهمة: | Keywords: In-hospital mortality; Intensive care unit; MIMIC IV database; Machine learning; Model prediction; Spontaneous intracerebral hemorrhage |
| تواريخ الأحداث: | Date Created: 20240620 Date Completed: 20240620 Latest Revision: 20240623 |
| رمز التحديث: | 20240623 |
| مُعرف محوري في PubMed: | PMC11190185 |
| DOI: | 10.1038/s41598-024-65128-8 |
| PMID: | 38902304 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-024-65128-8 |