Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants.
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| Title: | Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants. |
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| Authors: | Lim J; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Park SH; Institute of Medical Device and Robot, Kyungpook National University, Daegu 41566, Republic of Korea., Cha T; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Yoon SJ; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Han JH; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Shin JE; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Song IG; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Lee SM; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Eun HS; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea., Park MS; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea. |
| Source: | Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2026 Apr 24; Vol. 16 (9). Date of Electronic Publication: 2026 Apr 24. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101658402 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4418 (Print) Linking ISSN: 20754418 NLM ISO Abbreviation: Diagnostics (Basel) Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2075-4418 |
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| DOI: | 10.3390/diagnostics16091282 |
