Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.
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| Title: | Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study. |
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| Authors: | Song YX; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.; Medical School of Chinese People's Liberation Army, Beijing, China., Yang XD; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China., Luo YG; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.; Medical School of Chinese People's Liberation Army, Beijing, China., Ouyang CL; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Yu Y; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Ma YL; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Li H; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Lou JS; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Liu YH; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Chen YQ; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China., Cao JB; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China., Mi WD; Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China. |
| Source: | CNS neuroscience & therapeutics [CNS Neurosci Ther] 2023 Jan; Vol. 29 (1), pp. 158-167. Date of Electronic Publication: 2022 Oct 11. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101473265 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1755-5949 (Electronic) Linking ISSN: 17555930 NLM ISO Abbreviation: CNS Neurosci Ther Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1755-5949 |
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| DOI: | 10.1111/cns.13991 |
