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