Predicting Future Respiratory Hospitalizations in Extremely Premature Neonates Using Transcriptomic Data and Machine Learning.

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Bibliographic Details
Title: Predicting Future Respiratory Hospitalizations in Extremely Premature Neonates Using Transcriptomic Data and Machine Learning.
Authors: McOmber BG; Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA., Randolph L; Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA., Lang P 2nd; Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA., Kwinta P; Department of Pediatrics, Jagiellonian University Medical College, 30-663 Krakow, Poland., Kuiper J; Department of Environmental and Occupational Health, George Washington University, Washington, DC 20052, USA., Makker K; Department of Pediatrics, Johns Hopkins University, Baltimore, MD 21287, USA., Aziz KB; Department of Pediatrics, Johns Hopkins University, Baltimore, MD 21287, USA., Moreira A; Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Source: Children (Basel, Switzerland) [Children (Basel)] 2025 Jul 29; Vol. 12 (8). Date of Electronic Publication: 2025 Jul 29.
Publication Type: Journal Article
Journal Info: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101648936 Publication Model: Electronic Cited Medium: Print ISSN: 2227-9067 (Print) Linking ISSN: 22279067 NLM ISO Abbreviation: Children (Basel) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2227-9067
DOI:10.3390/children12080996