Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.
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| Title: | Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness. |
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| Authors: | Choi S; Department of Medicine, College of Medicine, Gachon University, Incheon, Republic of Korea., Kim YJ; Gachon Biomedical & Convergence Institute, Gil Medical Center, Gachon University, Incheon, Republic of Korea., Lee SM; Medical Devices R&D Center, Gachon University Gil Medical Center, Incheon, Republic of Korea.; Department of Biohealth & Medical Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea., Kim KG; Gachon Biomedical & Convergence Institute, Gil Medical Center, Gachon University, Incheon, Republic of Korea.; Medical Devices R&D Center, Gachon University Gil Medical Center, Incheon, Republic of Korea.; Department of Biohealth & Medical Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea.; Department of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University, Incheon, Republic of Korea. |
| Source: | Digital health [Digit Health] 2025 Apr 03; Vol. 11, pp. 20552076251325990. Date of Electronic Publication: 2025 Apr 03 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: SAGE Publications Ltd Country of Publication: United States NLM ID: 101690863 Publication Model: eCollection Cited Medium: Print ISSN: 2055-2076 (Print) Linking ISSN: 20552076 NLM ISO Abbreviation: Digit Health Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2055-2076 |
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| DOI: | 10.1177/20552076251325990 |
