Using Vital Signs for the Early Prediction of Necrotizing Enterocolitis in Preterm Neonates with Machine Learning
Bibliographic Data
| ID | 15719655 |
|---|---|
| Authors | Rosa Verhoeven (0000-0002-3564-9032, University Medical Center Groningen, corresponding author), Thijmen Kupers (0009-0000-4958-7488, University Medical Center Groningen), Celina L Brunsch (0000-0002-3394-7423, University Medical Center Groningen), Jan B F Hulscher (0000-0003-4491-486X, University Medical Center Groningen), Elisabeth M W Kooi (0000-0001-9195-4018, University Medical Center Groningen) |
| Year | 2024 |
| Volume | 11 |
| Issue | 12 |
| Pages | 1452-1452 |
| Publication date | 2024-11-28 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children (JOURNAL) |
| Journal identifiers | ISSN: 2227-9067 • E-ISSN: 2227-9067 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/children11121452 |
| PMID | 39767881 |
| OpenAlex | W4404823191 |
| Language | EN |
| References cited | 45 |
Using vital signs, we predicted NEC in the first 5 postnatal days with an F1-score up to 0.82. Splanchnic and cerebral oxygenation were the most contributing vital predictors. This pioneering effort in early NEC prediction using vital signs underscores the potential for targeted preventive measures and also emphasizes the need for additional data in future studies
Anesthesia · Gestational age · Logistic regression · Machine learning · Necrotizing Enterocolitis · Pregnancy · Vital signs · Computer Science · Infant Development and Preterm Care · Infant Nutrition and Health · Medicine · Neonatal Respiratory Health Research · Internal Medicine · Pediatrics
| Highly cited | No |
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