Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.
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| Title: | Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality. |
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| Authors: | Oh MY; Department of Pediatrics, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea., Lee JH; Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. jaeholee@amc.seoul.kr.; Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. jaeholee@amc.seoul.kr. |
| Source: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2026 Apr 17; Vol. 26 (1). Date of Electronic Publication: 2026 Apr 17. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE; In Process |
| Database: | MEDLINE Ultimate |
| ISSN: | 1472-6947 |
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| DOI: | 10.1186/s12911-026-03505-1 |
