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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.
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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  Data: Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants.
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  Data: <searchLink fieldCode="AU" term="%22Lim+J%22">Lim J</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+SH%22">Park SH</searchLink>; Institute of Medical Device and Robot, Kyungpook National University, Daegu 41566, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Cha+T%22">Cha T</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Yoon+SJ%22">Yoon SJ</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Han+JH%22">Han JH</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Shin+JE%22">Shin JE</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Song+IG%22">Song IG</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+SM%22">Lee SM</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Eun+HS%22">Eun HS</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+MS%22">Park MS</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101658402%22">Diagnostics (Basel, Switzerland)</searchLink> [Diagnostics (Basel)] 2026 Apr 24; Vol. 16 (9). <i>Date of Electronic Publication: </i>2026 Apr 24.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22MDPI+AG%22">MDPI AG </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101658402 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Print <i>ISSN: </i>2075-4418 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220754418%22">20754418 </searchLink><i>NLM ISO Abbreviation: </i>Diagnostics (Basel) <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.3390/diagnostics16091282
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        Text: English
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      – TitleFull: Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants.
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            – D: 24
              M: 04
              Text: 2026 Apr 24
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              Y: 2026
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