Identifying early-life, environmental, and social stressors as key predictors of U.S. respiratory failure mortality: a machine learning study.
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| Title: | Identifying early-life, environmental, and social stressors as key predictors of U.S. respiratory failure mortality: a machine learning study. |
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| Authors: | Yang C; Department of Emergency Medicine, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.; Department of Emergency, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, Fujian, China.; Department of Emergency Medicine, Fuzong Teaching Hospital of Fujian University of Traditional Chinese Medicine (900th Hospital), Fuzhou, Fujian, China., Zhang W; Department of Emergency Medicine, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.; Department of Emergency, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, Fujian, China.; Department of Emergency Medicine, Fuzong Teaching Hospital of Fujian University of Traditional Chinese Medicine (900th Hospital), Fuzhou, Fujian, China., Liu J; Department of Emergency Medicine, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.; Department of Emergency, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, Fujian, China.; Department of Emergency Medicine, Fuzong Teaching Hospital of Fujian University of Traditional Chinese Medicine (900th Hospital), Fuzhou, Fujian, China. |
| Source: | Frontiers in public health [Front Public Health] 2026 Jun 26; Vol. 14, pp. 1839753. Date of Electronic Publication: 2026 Jun 26 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2296-2565 |
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| DOI: | 10.3389/fpubh.2026.1839753 |
